Paper Figures

gc()

##          used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 410041 21.9     842488   45         NA   658114 35.2
## Vcells 795689  6.1    8388608   64     102400  1802080 13.8

rm(list=ls())
library(gam)

## Loading required package: splines

## Loading required package: foreach

## Loaded gam 1.20

library(scran)

## Loading required package: SingleCellExperiment

## Loading required package: SummarizedExperiment

## Loading required package: GenomicRanges

## Loading required package: stats4

## Loading required package: BiocGenerics

## Loading required package: parallel

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## Attaching package: 'BiocGenerics'

## The following objects are masked from 'package:parallel':
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##     clusterExport, clusterMap, parApply, parCapply, parLapply,
##     parLapplyLB, parRapply, parSapply, parSapplyLB

## The following objects are masked from 'package:stats':
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##     rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
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## Loading required package: GenomeInfoDb

## Loading required package: Biobase

## Welcome to Bioconductor
## 
##     Vignettes contain introductory material; view with
##     'browseVignettes()'. To cite Bioconductor, see
##     'citation("Biobase")', and for packages 'citation("pkgname")'.

## Loading required package: DelayedArray

## Loading required package: matrixStats

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library(ggplot2)
library(dynamicTreeCut)
library(umap)
library(ComplexHeatmap)

## Loading required package: grid

## ========================================
## ComplexHeatmap version 2.4.3
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
## 
## If you use it in published research, please cite:
## Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
##   genomic data. Bioinformatics 2016.
## 
## This message can be suppressed by:
##   suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================

library(gridExtra)

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library(grid)
library(viridis)

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library(destiny)

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library(circlize)

## ========================================
## circlize version 0.4.12
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
## 
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
##   in R. Bioinformatics 2014.
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## This message can be suppressed by:
##   suppressPackageStartupMessages(library(circlize))
## ========================================

library(ggforce)
library(edgeR)

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library(fpc)
library(Seurat)

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library(scater)

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#base='/Users/gabriele.lubatti/Desktop/Phd/Cell_Competition'
base_path="/Users/gabriele.lubatti/Desktop/Phd/Cell_Competition/Cell_Competition_Github/Input_data_Paper_Figures"

#setwd("/Users/gabriele.lubatti/Desktop/Phd/Cell_Competition/Script")
#load("data_tpm.Rda")
#load(file="data.Rda")
#load(file="good_cells.Rda")
#load(file="meta_dati.Rda")

#geni_comuni=intersect(row.names(data),row.names(data_tpm))
#data_raw=data[geni_comuni,colnames(data_tpm)]
#data_tpm=data_tpm[geni_comuni,]

setwd(base_path)
load(file='data_raw.Rda')
load(file='data_tpm.Rda')
#load(file='QC_start.Rda')
#load(file='meta_dati.Rda')
#load("HVGC_giusto_no_un.Rda")
#load("Gene_Name_ID.Rda")
#data_p53=read.csv("p53_full.csv",sep=";",header=T)
#up_p53_mouse=read.csv("up_p53_topo.csv",sep=",",header=T)
#down_p53_mouse=read.csv("down_p53_topo.csv",sep=",",header=T)
#geni_upr=read.csv2("ana_upr.csv",header=F)

#setwd(base_path)
#save(QC_start,meta_dati,HVGC,nome_gene,file="input_for_figures.Rdata")

setwd(base_path)
data_p53=read.csv("p53_full.csv",sep=";",header=T)
up_p53_mouse=read.csv("up_p53_mouse.csv",sep=",",header=T)
down_p53_mouse=read.csv("down_p53_mouse.csv",sep=",",header=T)
geni_upr=read.csv2("ana_upr.csv",header=F)

setwd(base_path)
load("input_for_figures.Rdata")

#setwd(base_path)
#load(file="meta_dati_course.Rda")
#cells_fmk=row.names(meta_dati_course)[meta_dati_course$condition=="Cell competition OFF"]
#cells_dmso=row.names(meta_dati_course)[meta_dati_course$condition=="Cell competition ON"]
#data_fmk=data_raw[,cells_fmk]
#data_dmso=data_raw[,cells_dmso]

#meta_fmk=meta_dati_course[cells_fmk,]
#meta_dmso=meta_dati_course[cells_dmso,]

#clu_fmk=meta_dati_course$cluster
#clu_fmk[clu_fmk==1]="Win Epi"
#clu_fmk[clu_fmk==2]="ExE"
#clu_fmk[clu_fmk==3]="Intermediate"
#clu_fmk[clu_fmk==4]="Los Epi"
#clu_fmk[clu_fmk==5]="VE"





#meta_fmk=meta_dati_course

#meta_fmk$cell_name=row.names(meta_fmk)
#meta_fmk$nomi_cluster=clu_fmk



#setwd("/Users/gabriele.lubatti/Desktop/Phd/Cell_Competition/Cell_Competition_Github/Input_data_Paper_Figures")
#write.table(meta_fmk,file="meta_all.txt",row.names = F)
#write.table(meta_dmso,file="meta_dmso.txt",row.names = F)
#setwd("/Users/gabriele.lubatti/Desktop/Phd/Cell_Competition/Cell_Competition_Github/Input_data_Paper_Figures")
#write.table(data_raw,file="data_raw.txt",row.names = F)
#write.table(data_dmso,file="data_dmso.txt",row.names = F)

#setwd("/Users/gabriele.lubatti/Desktop/Phd/Cell_Competition/SC_course")
#load(file="data_tpm_course.Rda")
#load(file="data_raw_course.Rda")
#load("meta_dati1.Rda")
#load("HVGC_giusto_no_un.Rda")
#load("Gene_Name_ID.Rda")

meta_dati=meta_dati[colnames(data_raw),]
Condition=rep(0,length(meta_dati$Sample))
Condition[grep("FMK",meta_dati$Sample)]="Cell competition OFF"
Condition[grep("DMSO",meta_dati$Sample)]="Cell competition ON"

Extended Figure 1 A

QC_start_good=QC_start
QC_start_good$quality_control="Good Cells"
pw1=ggplot(QC_start_good, aes(QC_start_good$quality_control,QC_start_good$numAbove10rpm)) +
  geom_boxplot() +ggtitle("QC (723 cells)")+ylab("numAbove10rpm")+xlab(NULL)
pw2=ggplot(QC_start_good, aes(QC_start_good$quality_control,QC_start_good$fracSpikes)) +
  geom_boxplot() +ggtitle("QC (723 cells)")+ylab("Fraction of spikes")+xlab(NULL)

pw3=ggplot(QC_start_good, aes(QC_start_good$quality_control,QC_start_good$fracMTs)) +
  geom_boxplot() +ggtitle("QC (723 cells)")+ylab("Fraction of MTs")+xlab(NULL)

pw4=ggplot(QC_start_good, aes(QC_start_good$quality_control,QC_start_good$fracGenes)) +
  geom_boxplot() +ggtitle("QC (723 cells)")+ylab("Fraction of Genes")+xlab(NULL)


pw5=ggplot(QC_start_good, aes(QC_start_good$quality_control,QC_start_good$fracMappedReads)) +
  geom_boxplot() +ggtitle("QC (723 cells)")+ylab("Frac of Mapped Reads")+xlab(NULL)

pw6=ggplot(QC_start_good, aes(QC_start_good$quality_control,QC_start_good$totReadsLog10)) +
  geom_boxplot() +ggtitle("QC (723 cells)")+ylab("Tot number of reads(Log10)")+xlab(NULL)

grid.arrange(pw1,pw2,pw3,pw4,pw5,pw6,ncol=3,nrow=2)

## Warning: Use of `QC_start_good$quality_control` is discouraged. Use
## `quality_control` instead.

## Warning: Use of `QC_start_good$numAbove10rpm` is discouraged. Use
## `numAbove10rpm` instead.

## Warning: Use of `QC_start_good$quality_control` is discouraged. Use
## `quality_control` instead.

## Warning: Use of `QC_start_good$fracSpikes` is discouraged. Use `fracSpikes`
## instead.

## Warning: Use of `QC_start_good$quality_control` is discouraged. Use
## `quality_control` instead.

## Warning: Use of `QC_start_good$fracMTs` is discouraged. Use `fracMTs` instead.

## Warning: Use of `QC_start_good$quality_control` is discouraged. Use
## `quality_control` instead.

## Warning: Use of `QC_start_good$fracGenes` is discouraged. Use `fracGenes`
## instead.

## Warning: Use of `QC_start_good$quality_control` is discouraged. Use
## `quality_control` instead.

## Warning: Use of `QC_start_good$fracMappedReads` is discouraged. Use
## `fracMappedReads` instead.

## Warning: Use of `QC_start_good$quality_control` is discouraged. Use
## `quality_control` instead.

## Warning: Use of `QC_start_good$totReadsLog10` is discouraged. Use
## `totReadsLog10` instead.

Figure 1 D

log_data_tpm <- log10(data_tpm[HVGC,]+1)

log_data_tpm<-as.matrix(log_data_tpm)
dissimilarity <- sqrt((1-cor((log_data_tpm), method = "spearman"))/2)

my.dist <- as.dist(dissimilarity) 
set.seed(100)
object<-umap(d=as.matrix(my.dist),input="dist")

umap_met<-as.data.frame(object$layout)


qs_upa<-ggplot(as.data.frame(object$layout), aes(umap_met[,1],umap_met[,2],color=as.factor(Condition) )) +
  geom_point()
qs_upa+scale_color_manual(values=c("green 1", "yellow 1"))+ labs(colour = "Condition")+labs(x = "Umap1",y="Umap2") + ggtitle(NULL)

Figure 1 C

my.tree <- hclust(my.dist
                  , method="ward.D2" # met = "ward.D2" was adopted form the reference
                  )
my.clusters <- cutreeHybrid(my.tree
                            ,distM=as.matrix(my.dist),deepSplit=0
                            ,minClusterSize = 35)$label#5)$label

##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.

length(unique(my.clusters))

## [1] 5

classe<-data.frame(Cell_type = meta_dati$Sample
                                          ,cluster = my.clusters)
classe$condizione=Condition 



table(classe$cluster,classe$condizione)

##    
##     Cell competition OFF Cell competition ON
##   1                   78                 229
##   2                   77                  94
##   3                  125                   9
##   4                   71                   0
##   5                   16                  24

Cluster=as.factor(classe$cluster)

sp<-ggplot(as.data.frame(object$layout),aes(umap_met[,1],umap_met[,2],color=Cluster)) + geom_point()
sp + scale_color_manual(values=c("#DD6400", "#800080","#0000FF", "#006400","#56B4E9","#A9A9A9"),labels=c("Normal (winner) Epiblast (78FMK/229DMSO) ","Extra Embrionic Ectoderm (77FMK/94DMSO)","Intermediate (125FMK/9DMSO)","Loser Epiblast (71FMK/0 DMSO)","Visceral Endoderm (16FMK/24DMSO)"))+labs(x = "Umap1",y="Umap2") + ggtitle(NULL)

#setwd("/Users/gabriele.lubatti/Desktop/Phd/entropy_of_mixing_library/teichman_data")
#load("gene_id_name_table.RData")
#row.names(temp)=as.character(temp$Gene_stable_ID)


#gene.name.id<-as.character(temp[,"Gene_stable_ID"])
#names(gene.name.id)<-as.character(temp[,"Gene_name"])

#geni_mt_id=row.names(data_raw)[row.names(data_raw)%in%as.vector(gene.name.id[grep("mt-",names(gene.na#me.id))])]

#cells_epi=colnames(data_raw)[classe$cluster==1|classe$cluster==3|classe$cluster==4]


#row.names(classe)=colnames(data_raw)
#classe_epi=classe[cells_epi,]
#classe_epi=classe_epi[classe_epi$condizione=="Cell competition OFF",]
#cluster_condition=rep(0,length(classe_epi$cluster))
#cluster_condition[classe_epi$cluster==1&classe_epi$condizione=="Cell competition OFF"]="Winner cells"
#cluster_condition[classe_epi$cluster==3&classe_epi$condizione=="Cell competition OFF"]="Intermediate #cells"
#cluster_condition[classe_epi$cluster==4&classe_epi$condizione=="Cell competition OFF"]="Loser cells"
#cluster_condition[classe_epi$cluster==1&classe_epi$condizione=="Cell competition ON"]="Win Epi DMSO"
#cluster_condition[classe_epi$cluster==3&classe_epi$condizione=="Cell competition ON"]="Int Epi DMSO"

#sum_mt=apply(data_raw[geni_mt_id,row.names(classe_epi)],2,sum)
#cluster_condition=factor(cluster_condition,levels=c("Winner cells","Intermediate cells","Loser #cells"))
#data_plot=data.frame(cluster_condition,sum_mt)

#p<-ggplot(data_plot, aes(x=cluster_condition, y=sum_mt, fill=cluster_condition)) +
  #geom_boxplot()+labs(fill="Cluster")+ylab("Number of reads mapping to mtDNA")+xlab("Cluster ")
#p+scale_fill_manual(values=c("#DD6400", "#0000FF", "#006400"))+ggtitle("mtDNA coverage for CI treated #cells")

#geni_mt_id=row.names(data_raw)[row.names(data_raw)%in%as.vector(gene.name.id[grep("mt-Rnr",names(gene#.name.id))])]
#sum_mt=apply(data_raw[geni_mt_id,row.names(classe_epi)],2,sum)
#cluster_condition=factor(cluster_condition,levels=c("Win Epi CI","Intermediate Epi CI","Los Epi CI"))
#data_plot=data.frame(cluster_condition,sum_mt)

#p<-ggplot(data_plot, aes(x=cluster_condition, y=sum_mt, fill=cluster_condition)) +
  #geom_boxplot()+labs(fill="Cluster")+ylab("mtDNA content")+xlab("Cluster ")
#p+scale_fill_manual(values=c("#DD6400", "#0000FF", "#006400"))+ggtitle("mtDNA content from only #mt-Rnr1 and mt-Rnr2")

Extended Figure 1 F

my.clusters_0 <- cutreeHybrid(my.tree
                            ,distM=as.matrix(my.dist),deepSplit=0
                            ,minClusterSize = 35)$label#5)$label

##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.

my.clusters_1 <- cutreeHybrid(my.tree
                            ,distM=as.matrix(my.dist),deepSplit=1
                            ,minClusterSize = 35)$label#5)$label

##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.

my.clusters_0[my.clusters_0==1]="Win Epi"
my.clusters_0[my.clusters_0==2]="ExE"
my.clusters_0[my.clusters_0==3]="Intermediate"
my.clusters_0[my.clusters_0==4]="Los Epi"
my.clusters_0[my.clusters_0==5]="VE"
classe_new<-data.frame(deepSplit0 = my.clusters_0,deepSplit1=my.clusters_1)

library(clustree)

## Loading required package: ggraph

clustree(classe_new,prefix="deepSplit")

## Warning: The `add` argument of `group_by()` is deprecated as of dplyr 1.0.0.
## Please use the `.add` argument instead.

Extended Figure 1 E

data_tpm_log=log10(data_tpm+1)
# mr of repeats for bootsraping
nrRepeats = 100
# percent of gHVGs to use
percent = 60
# nr of HVGs left
sampleSize <- round(length(HVGC)*percent/100)
myBootstrap <- function(indexVector){
        idxMatrix <- matrix(rep(NA, sampleSize*nrRepeats)
                            ,nrow = nrRepeats)
        for (i in 1:nrow(idxMatrix)){
                mySample <- sample(indexVector
                                   ,size = sampleSize)
                idxMatrix[i,] <- mySample
        }
        idxMatrix
}
# indices of all HVGs
HVGidx <- which(rownames(data_tpm_log) %in% HVGC)
bootstrapIdxMatrix <- myBootstrap(HVGidx)
# function to calculated clusters
claculateClusters <- function(geneIndices,deepSplit){
        chosen.exprs <- (data_tpm_log)[geneIndices,]
        # Calculate distance matrix between cells
        dissimilarity <- sqrt((1-cor(chosen.exprs, method = "spearman"))/2)
        my.dist <- as.dist(dissimilarity) 
        my.tree <- hclust(my.dist
                          , method="ward.D2" # method = "ward.D2" was adopted form the reference https://f1000research.com/articles/5-2122/v1 NOTE: next time take method "average"
                          # some good reference about the different methods is here: https://cran.r-project.org/web/packages/dendextend/vignettes/Cluster_Analysis.html#the-3-clusters-from-the-complete-method-vs-the-real-species-category
                          )
        # Cut the treee to identify clusters
        my.clusters <- cutreeHybrid(my.tree
                                    ,distM=as.matrix(my.dist)
                                    ,deepSplit = deepSplit
                                    )$label
        
        #names(my.clusters) <- clean_annotation$Run_Lane
        # calculate the cluster statistics
        clusterStatistics <- cluster.stats(d = my.dist
                                           ,clustering = my.clusters)
        
        result <- list("PearsonGamma" = clusterStatistics$pearsongamma
                       ,"avgSilhouetteWidth" = clusterStatistics$avg.silwidth)
        result
}

# dummy vector of Pearson
        
        # Cluster the data
        # Make hierarchical clustering
# dummy vector of Pearson Gammas for 5 deepSplit values and 100 bootstrap repeats
PearsonGammaVector <- matrix(rep(NA, nrRepeats * 5)
                             ,ncol = 5)
colnames(PearsonGammaVector) <- c(0,1,2,3,4)

# dummy vector of average silhouette width for 5 deepSplit values and 100 bootstrap repeats
avgSilhouetteWidth <- matrix(rep(NA, nrRepeats * 5)
                             ,ncol = 5)
colnames(avgSilhouetteWidth) <- c(0,1,2,3,4)
for(j in 0:4){
        for (i in 1:nrow(bootstrapIdxMatrix)){
                print(paste("i is", i, ", j is" ,j)) # just to keep track of the iterations???
                # take indices of HVGs from the bootstrapIdxMatrix of the i-th iteration
                indices <- bootstrapIdxMatrix[i,]
                # calculate the cluster statistics with these indices and deepSplit == j
                myResults <- claculateClusters(indices,j)
                
                # save the Pearsom gammma results
                PearsonGammaVector[i,j+1] <- myResults$PearsonGamma
                # save the average silhouette width results
                avgSilhouetteWidth[i,j+1] <- myResults$avgSilhouetteWidth    
        }
}

## [1] "i is 1 , j is 0"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 2 , j is 0"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 3 , j is 0"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 4 , j is 0"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 5 , j is 0"
##  ..cutHeight not given, setting it to 4.11  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 6 , j is 0"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 7 , j is 0"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 8 , j is 0"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 9 , j is 0"
##  ..cutHeight not given, setting it to 4.24  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 10 , j is 0"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 11 , j is 0"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 12 , j is 0"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 13 , j is 0"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 14 , j is 0"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 15 , j is 0"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 16 , j is 0"
##  ..cutHeight not given, setting it to 4.08  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 17 , j is 0"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 18 , j is 0"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 19 , j is 0"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 20 , j is 0"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 21 , j is 0"
##  ..cutHeight not given, setting it to 4.05  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 22 , j is 0"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 23 , j is 0"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 24 , j is 0"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 25 , j is 0"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 26 , j is 0"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 27 , j is 0"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 28 , j is 0"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 29 , j is 0"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 30 , j is 0"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 31 , j is 0"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 32 , j is 0"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 33 , j is 0"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 34 , j is 0"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 35 , j is 0"
##  ..cutHeight not given, setting it to 4.45  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 36 , j is 0"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 37 , j is 0"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 38 , j is 0"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 39 , j is 0"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 40 , j is 0"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 41 , j is 0"
##  ..cutHeight not given, setting it to 4.15  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 42 , j is 0"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 43 , j is 0"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 44 , j is 0"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 45 , j is 0"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 46 , j is 0"
##  ..cutHeight not given, setting it to 4.44  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 47 , j is 0"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 48 , j is 0"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 49 , j is 0"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 50 , j is 0"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 51 , j is 0"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 52 , j is 0"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 53 , j is 0"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 54 , j is 0"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 55 , j is 0"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 56 , j is 0"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 57 , j is 0"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 58 , j is 0"
##  ..cutHeight not given, setting it to 4.09  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 59 , j is 0"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 60 , j is 0"
##  ..cutHeight not given, setting it to 4.43  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 61 , j is 0"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 62 , j is 0"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 63 , j is 0"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 64 , j is 0"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 65 , j is 0"
##  ..cutHeight not given, setting it to 4.18  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 66 , j is 0"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 67 , j is 0"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 68 , j is 0"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 69 , j is 0"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 70 , j is 0"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 71 , j is 0"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 72 , j is 0"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 73 , j is 0"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 74 , j is 0"
##  ..cutHeight not given, setting it to 4.1  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 75 , j is 0"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 76 , j is 0"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 77 , j is 0"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 78 , j is 0"
##  ..cutHeight not given, setting it to 4.52  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 79 , j is 0"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 80 , j is 0"
##  ..cutHeight not given, setting it to 4.46  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 81 , j is 0"
##  ..cutHeight not given, setting it to 4.42  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 82 , j is 0"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 83 , j is 0"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 84 , j is 0"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 85 , j is 0"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 86 , j is 0"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 87 , j is 0"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 88 , j is 0"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 89 , j is 0"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 90 , j is 0"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 91 , j is 0"
##  ..cutHeight not given, setting it to 4.5  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 92 , j is 0"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 93 , j is 0"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 94 , j is 0"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 95 , j is 0"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 96 , j is 0"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 97 , j is 0"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 98 , j is 0"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 99 , j is 0"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 100 , j is 0"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 1 , j is 1"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 2 , j is 1"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 3 , j is 1"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 4 , j is 1"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 5 , j is 1"
##  ..cutHeight not given, setting it to 4.11  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 6 , j is 1"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 7 , j is 1"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 8 , j is 1"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 9 , j is 1"
##  ..cutHeight not given, setting it to 4.24  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 10 , j is 1"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 11 , j is 1"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 12 , j is 1"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 13 , j is 1"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 14 , j is 1"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 15 , j is 1"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 16 , j is 1"
##  ..cutHeight not given, setting it to 4.08  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 17 , j is 1"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 18 , j is 1"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 19 , j is 1"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 20 , j is 1"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 21 , j is 1"
##  ..cutHeight not given, setting it to 4.05  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 22 , j is 1"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 23 , j is 1"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 24 , j is 1"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 25 , j is 1"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 26 , j is 1"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 27 , j is 1"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 28 , j is 1"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 29 , j is 1"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 30 , j is 1"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 31 , j is 1"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 32 , j is 1"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 33 , j is 1"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 34 , j is 1"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 35 , j is 1"
##  ..cutHeight not given, setting it to 4.45  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 36 , j is 1"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 37 , j is 1"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 38 , j is 1"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 39 , j is 1"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 40 , j is 1"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 41 , j is 1"
##  ..cutHeight not given, setting it to 4.15  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 42 , j is 1"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 43 , j is 1"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 44 , j is 1"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 45 , j is 1"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 46 , j is 1"
##  ..cutHeight not given, setting it to 4.44  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 47 , j is 1"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 48 , j is 1"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 49 , j is 1"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 50 , j is 1"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 51 , j is 1"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 52 , j is 1"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 53 , j is 1"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 54 , j is 1"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 55 , j is 1"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 56 , j is 1"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 57 , j is 1"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 58 , j is 1"
##  ..cutHeight not given, setting it to 4.09  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 59 , j is 1"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 60 , j is 1"
##  ..cutHeight not given, setting it to 4.43  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 61 , j is 1"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 62 , j is 1"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 63 , j is 1"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 64 , j is 1"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 65 , j is 1"
##  ..cutHeight not given, setting it to 4.18  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 66 , j is 1"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 67 , j is 1"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 68 , j is 1"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 69 , j is 1"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 70 , j is 1"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 71 , j is 1"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 72 , j is 1"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 73 , j is 1"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 74 , j is 1"
##  ..cutHeight not given, setting it to 4.1  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 75 , j is 1"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 76 , j is 1"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 77 , j is 1"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 78 , j is 1"
##  ..cutHeight not given, setting it to 4.52  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 79 , j is 1"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 80 , j is 1"
##  ..cutHeight not given, setting it to 4.46  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 81 , j is 1"
##  ..cutHeight not given, setting it to 4.42  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 82 , j is 1"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 83 , j is 1"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 84 , j is 1"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 85 , j is 1"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 86 , j is 1"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 87 , j is 1"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 88 , j is 1"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 89 , j is 1"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 90 , j is 1"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 91 , j is 1"
##  ..cutHeight not given, setting it to 4.5  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 92 , j is 1"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 93 , j is 1"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 94 , j is 1"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 95 , j is 1"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 96 , j is 1"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 97 , j is 1"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 98 , j is 1"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 99 , j is 1"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 100 , j is 1"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 1 , j is 2"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 2 , j is 2"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 3 , j is 2"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 4 , j is 2"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 5 , j is 2"
##  ..cutHeight not given, setting it to 4.11  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 6 , j is 2"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 7 , j is 2"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 8 , j is 2"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 9 , j is 2"
##  ..cutHeight not given, setting it to 4.24  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 10 , j is 2"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 11 , j is 2"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 12 , j is 2"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 13 , j is 2"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 14 , j is 2"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 15 , j is 2"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 16 , j is 2"
##  ..cutHeight not given, setting it to 4.08  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 17 , j is 2"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 18 , j is 2"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 19 , j is 2"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 20 , j is 2"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 21 , j is 2"
##  ..cutHeight not given, setting it to 4.05  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 22 , j is 2"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 23 , j is 2"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 24 , j is 2"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 25 , j is 2"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 26 , j is 2"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 27 , j is 2"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 28 , j is 2"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 29 , j is 2"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 30 , j is 2"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 31 , j is 2"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 32 , j is 2"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 33 , j is 2"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 34 , j is 2"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 35 , j is 2"
##  ..cutHeight not given, setting it to 4.45  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 36 , j is 2"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 37 , j is 2"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 38 , j is 2"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 39 , j is 2"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 40 , j is 2"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 41 , j is 2"
##  ..cutHeight not given, setting it to 4.15  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 42 , j is 2"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 43 , j is 2"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 44 , j is 2"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 45 , j is 2"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 46 , j is 2"
##  ..cutHeight not given, setting it to 4.44  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 47 , j is 2"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 48 , j is 2"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 49 , j is 2"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 50 , j is 2"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 51 , j is 2"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 52 , j is 2"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 53 , j is 2"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 54 , j is 2"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 55 , j is 2"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 56 , j is 2"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 57 , j is 2"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 58 , j is 2"
##  ..cutHeight not given, setting it to 4.09  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 59 , j is 2"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 60 , j is 2"
##  ..cutHeight not given, setting it to 4.43  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 61 , j is 2"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 62 , j is 2"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 63 , j is 2"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 64 , j is 2"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 65 , j is 2"
##  ..cutHeight not given, setting it to 4.18  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 66 , j is 2"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 67 , j is 2"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 68 , j is 2"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 69 , j is 2"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 70 , j is 2"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 71 , j is 2"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 72 , j is 2"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 73 , j is 2"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 74 , j is 2"
##  ..cutHeight not given, setting it to 4.1  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 75 , j is 2"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 76 , j is 2"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 77 , j is 2"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 78 , j is 2"
##  ..cutHeight not given, setting it to 4.52  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 79 , j is 2"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 80 , j is 2"
##  ..cutHeight not given, setting it to 4.46  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 81 , j is 2"
##  ..cutHeight not given, setting it to 4.42  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 82 , j is 2"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 83 , j is 2"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 84 , j is 2"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 85 , j is 2"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 86 , j is 2"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 87 , j is 2"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 88 , j is 2"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 89 , j is 2"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 90 , j is 2"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 91 , j is 2"
##  ..cutHeight not given, setting it to 4.5  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 92 , j is 2"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 93 , j is 2"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 94 , j is 2"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 95 , j is 2"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 96 , j is 2"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 97 , j is 2"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 98 , j is 2"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 99 , j is 2"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 100 , j is 2"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 1 , j is 3"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 2 , j is 3"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 3 , j is 3"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 4 , j is 3"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 5 , j is 3"
##  ..cutHeight not given, setting it to 4.11  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 6 , j is 3"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 7 , j is 3"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 8 , j is 3"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 9 , j is 3"
##  ..cutHeight not given, setting it to 4.24  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 10 , j is 3"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 11 , j is 3"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 12 , j is 3"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 13 , j is 3"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 14 , j is 3"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 15 , j is 3"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 16 , j is 3"
##  ..cutHeight not given, setting it to 4.08  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 17 , j is 3"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 18 , j is 3"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 19 , j is 3"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 20 , j is 3"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 21 , j is 3"
##  ..cutHeight not given, setting it to 4.05  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 22 , j is 3"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 23 , j is 3"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 24 , j is 3"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 25 , j is 3"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 26 , j is 3"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 27 , j is 3"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 28 , j is 3"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 29 , j is 3"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 30 , j is 3"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 31 , j is 3"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 32 , j is 3"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 33 , j is 3"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 34 , j is 3"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 35 , j is 3"
##  ..cutHeight not given, setting it to 4.45  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 36 , j is 3"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 37 , j is 3"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 38 , j is 3"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 39 , j is 3"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 40 , j is 3"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 41 , j is 3"
##  ..cutHeight not given, setting it to 4.15  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 42 , j is 3"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 43 , j is 3"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 44 , j is 3"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 45 , j is 3"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 46 , j is 3"
##  ..cutHeight not given, setting it to 4.44  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 47 , j is 3"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 48 , j is 3"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 49 , j is 3"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 50 , j is 3"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 51 , j is 3"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 52 , j is 3"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 53 , j is 3"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 54 , j is 3"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 55 , j is 3"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 56 , j is 3"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 57 , j is 3"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 58 , j is 3"
##  ..cutHeight not given, setting it to 4.09  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 59 , j is 3"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 60 , j is 3"
##  ..cutHeight not given, setting it to 4.43  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 61 , j is 3"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 62 , j is 3"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 63 , j is 3"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 64 , j is 3"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 65 , j is 3"
##  ..cutHeight not given, setting it to 4.18  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 66 , j is 3"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 67 , j is 3"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 68 , j is 3"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 69 , j is 3"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 70 , j is 3"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 71 , j is 3"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 72 , j is 3"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 73 , j is 3"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 74 , j is 3"
##  ..cutHeight not given, setting it to 4.1  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 75 , j is 3"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 76 , j is 3"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 77 , j is 3"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 78 , j is 3"
##  ..cutHeight not given, setting it to 4.52  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 79 , j is 3"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 80 , j is 3"
##  ..cutHeight not given, setting it to 4.46  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 81 , j is 3"
##  ..cutHeight not given, setting it to 4.42  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 82 , j is 3"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 83 , j is 3"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 84 , j is 3"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 85 , j is 3"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 86 , j is 3"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 87 , j is 3"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 88 , j is 3"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 89 , j is 3"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 90 , j is 3"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 91 , j is 3"
##  ..cutHeight not given, setting it to 4.5  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 92 , j is 3"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 93 , j is 3"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 94 , j is 3"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 95 , j is 3"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 96 , j is 3"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 97 , j is 3"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 98 , j is 3"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 99 , j is 3"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 100 , j is 3"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 1 , j is 4"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 2 , j is 4"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 3 , j is 4"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 4 , j is 4"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 5 , j is 4"
##  ..cutHeight not given, setting it to 4.11  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 6 , j is 4"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 7 , j is 4"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 8 , j is 4"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 9 , j is 4"
##  ..cutHeight not given, setting it to 4.24  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 10 , j is 4"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 11 , j is 4"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 12 , j is 4"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 13 , j is 4"
##  ..cutHeight not given, setting it to 4.2  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 14 , j is 4"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 15 , j is 4"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 16 , j is 4"
##  ..cutHeight not given, setting it to 4.08  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 17 , j is 4"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 18 , j is 4"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 19 , j is 4"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 20 , j is 4"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 21 , j is 4"
##  ..cutHeight not given, setting it to 4.05  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 22 , j is 4"
##  ..cutHeight not given, setting it to 4.37  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 23 , j is 4"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 24 , j is 4"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 25 , j is 4"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 26 , j is 4"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 27 , j is 4"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 28 , j is 4"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 29 , j is 4"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 30 , j is 4"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 31 , j is 4"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 32 , j is 4"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 33 , j is 4"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 34 , j is 4"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 35 , j is 4"
##  ..cutHeight not given, setting it to 4.45  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 36 , j is 4"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 37 , j is 4"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 38 , j is 4"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 39 , j is 4"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 40 , j is 4"
##  ..cutHeight not given, setting it to 4.12  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 41 , j is 4"
##  ..cutHeight not given, setting it to 4.15  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 42 , j is 4"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 43 , j is 4"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 44 , j is 4"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 45 , j is 4"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 46 , j is 4"
##  ..cutHeight not given, setting it to 4.44  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 47 , j is 4"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 48 , j is 4"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 49 , j is 4"
##  ..cutHeight not given, setting it to 4.23  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 50 , j is 4"
##  ..cutHeight not given, setting it to 4.22  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 51 , j is 4"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 52 , j is 4"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 53 , j is 4"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 54 , j is 4"
##  ..cutHeight not given, setting it to 4.16  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 55 , j is 4"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 56 , j is 4"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 57 , j is 4"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 58 , j is 4"
##  ..cutHeight not given, setting it to 4.09  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 59 , j is 4"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 60 , j is 4"
##  ..cutHeight not given, setting it to 4.43  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 61 , j is 4"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 62 , j is 4"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 63 , j is 4"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 64 , j is 4"
##  ..cutHeight not given, setting it to 4.17  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 65 , j is 4"
##  ..cutHeight not given, setting it to 4.18  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 66 , j is 4"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 67 , j is 4"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 68 , j is 4"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 69 , j is 4"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 70 , j is 4"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 71 , j is 4"
##  ..cutHeight not given, setting it to 4.21  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 72 , j is 4"
##  ..cutHeight not given, setting it to 4.27  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 73 , j is 4"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 74 , j is 4"
##  ..cutHeight not given, setting it to 4.1  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 75 , j is 4"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 76 , j is 4"
##  ..cutHeight not given, setting it to 4.28  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 77 , j is 4"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 78 , j is 4"
##  ..cutHeight not given, setting it to 4.52  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 79 , j is 4"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 80 , j is 4"
##  ..cutHeight not given, setting it to 4.46  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 81 , j is 4"
##  ..cutHeight not given, setting it to 4.42  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 82 , j is 4"
##  ..cutHeight not given, setting it to 4.25  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 83 , j is 4"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 84 , j is 4"
##  ..cutHeight not given, setting it to 4.33  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 85 , j is 4"
##  ..cutHeight not given, setting it to 4.19  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 86 , j is 4"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 87 , j is 4"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 88 , j is 4"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 89 , j is 4"
##  ..cutHeight not given, setting it to 4.34  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 90 , j is 4"
##  ..cutHeight not given, setting it to 4.26  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 91 , j is 4"
##  ..cutHeight not given, setting it to 4.5  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 92 , j is 4"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 93 , j is 4"
##  ..cutHeight not given, setting it to 4.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 94 , j is 4"
##  ..cutHeight not given, setting it to 4.39  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 95 , j is 4"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 96 , j is 4"
##  ..cutHeight not given, setting it to 4.32  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 97 , j is 4"
##  ..cutHeight not given, setting it to 4.29  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 98 , j is 4"
##  ..cutHeight not given, setting it to 4.4  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 99 , j is 4"
##  ..cutHeight not given, setting it to 4.38  ===>  99% of the (truncated) height range in dendro.
##  ..done.
## [1] "i is 100 , j is 4"
##  ..cutHeight not given, setting it to 4.36  ===>  99% of the (truncated) height range in dendro.
##  ..done.

pearsonGammaBoxplot <- boxplot(PearsonGammaVector
                               ,use.cols = TRUE
                               ,main = "Pearson Gamma"
                               ,xlab = "deepSplit"
                               ,ylab = "Pearson Gamma"
                               )

pearsonGammaBoxplot

## $stats
##           [,1]      [,2]      [,3]      [,4]      [,5]
## [1,] 0.6453768 0.6746437 0.4780571 0.2711807 0.2292184
## [2,] 0.6725865 0.7028253 0.5210694 0.2972013 0.2571013
## [3,] 0.6848418 0.7118417 0.5474299 0.3108514 0.2678744
## [4,] 0.6960932 0.7218877 0.5822492 0.3233165 0.2768857
## [5,] 0.7213358 0.7387877 0.6646111 0.3606915 0.2997035
## 
## $n
## [1] 100 100 100 100 100
## 
## $conf
##           [,1]      [,2]      [,3]      [,4]      [,5]
## [1,] 0.6811277 0.7088298 0.5377635 0.3067252 0.2647485
## [2,] 0.6885558 0.7148535 0.5570963 0.3149776 0.2710004
## 
## $out
##  [1] 0.6725404 0.7065068 0.7074678 0.7095835 0.7153105 0.3627821 0.2577033
##  [8] 0.3659581 0.3913694 0.3103310 0.2264069
## 
## $group
##  [1] 2 3 3 3 3 4 4 4 4 5 5
## 
## $names
## [1] "0" "1" "2" "3" "4"

avgSilWidth <- boxplot(avgSilhouetteWidth
                       ,use.cols = TRUE
                       ,main = "Average Silhouette Width"
                       ,xlab = "deepSplit"
                       ,ylab = "Average Silhouette Width"
                       )

avgSilWidth

## $stats
##           [,1]      [,2]       [,3]        [,4]        [,5]
## [1,] 0.1150810 0.1167279 0.05311246 0.007183185 0.002651091
## [2,] 0.1207772 0.1218660 0.05973173 0.011508356 0.008094263
## [3,] 0.1245282 0.1252157 0.06217317 0.013632014 0.010221661
## [4,] 0.1270027 0.1271003 0.06463196 0.015648046 0.011810760
## [5,] 0.1359728 0.1333532 0.07030367 0.020342627 0.016083051
## 
## $n
## [1] 100 100 100 100 100
## 
## $conf
##           [,1]      [,2]       [,3]       [,4]        [,5]
## [1,] 0.1235445 0.1243887 0.06139894 0.01297794 0.009634454
## [2,] 0.1255118 0.1260427 0.06294741 0.01428609 0.010808867
## 
## $out
##  [1] 0.1104956767 0.1098077069 0.1119631908 0.1178714581 0.1274893688
##  [6] 0.0437664888 0.0728402279 0.0437710142 0.0411813026 0.0443882455
## [11] 0.0728568794 0.1032799902 0.0464366720 0.1151405182 0.0419362484
## [16] 0.0802955256 0.0019551496 0.0008981727 0.0024309241
## 
## $group
##  [1] 1 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5
## 
## $names
## [1] "0" "1" "2" "3" "4"

Extended Figure 1 D

batch=meta_dati$Batch
sp<-ggplot(as.data.frame(object$layout),aes(umap_met[,1],umap_met[,2],color=as.factor(batch)) )+ geom_point()+
  labs(x = "Umap1",y="Umap2",col="Batch") + ggtitle(NULL)
sp

Figure 1 E

fmk_1=sum(classe$cluster==1&classe$condizione=="Cell competition ON")/sum(classe$condizione=="Cell competition OFF"&(classe$cluster==1))
fmk_3=sum(classe$cluster==3&classe$condizione=="Cell competition ON")/sum(classe$condizione=="Cell competition OFF"&(classe$cluster==3))
fmk_4=sum(classe$cluster==4&classe$condizione=="Cell competition ON")/sum(classe$condizione=="Cell competition OFF"&(classe$cluster==4))
FMK=c(fmk_1,fmk_3,fmk_4)
data.frame(FMK)

##        FMK
## 1 2.935897
## 2 0.072000
## 3 0.000000

correlazione=round(data.frame(FMK),2)
colnames(correlazione)="DMSO/CI"
row.names(correlazione)=c("Cluster 1-Normal","Cluster 3-Intermediate","Cluster 4-Loser")

ht21 = Heatmap((correlazione)# heat.vals_Mutant_Paired #
               ,cluster_rows = F
                  , col= colorRamp2(c((min(correlazione)),1, round(max(correlazione))), c("blue","grey" ,"red"))
                  , name = "Enrichment"
                  , column_title = "Epiblast cells (512)"
                  #, cluster_columns = as.dendrogram(my.tree)
, cluster_columns = F
                  #, cluster_rows = TRUE #=as.dendrogram(as.numeric(row.names(log_data_tpm)[1:10]) )
                  #, top_annotation = haCol1
                  , row_names_gp = gpar(fontsize = 10
                                       #,col = geneColors
                   )

,column_names_gp = gpar(fontsize=14)
                  , show_column_names = T
                  , show_row_names = T
                  )

## Warning: The input is a data frame, convert it to the matrix.

draw(ht21
    #,column_title = "Absolute values", column_title_side = "top"
)

Extended Figure 1 B and C

#table(classe$cluster,classe$condizione)
Cluster=as.factor(classe$cluster)

classe$batch=batch

dati=table(as.factor(classe$cluster),as.factor(classe$batch))
dati_2=table(classe$condizione,classe$batch)
dati

##    
##       1   2   3   4   5
##   1 147  81  57   7  15
##   2  45  65  44   7  10
##   3  46  34  35  13   6
##   4  23  18  21   2   7
##   5   7  17   7   2   7

dati_2

##                       
##                          1   2   3   4   5
##   Cell competition OFF 136 105  86  16  24
##   Cell competition ON  132 110  78  15  21

meta_dati_course=data.frame(batch,classe$cluster,classe$condizione)
colnames(meta_dati_course)=c("batch","cluster","condition")
row.names(meta_dati_course)=colnames(data_raw)
good=colnames(data_raw)
setwd(base_path)
save(meta_dati_course,file="meta_dati_course.Rda")

Figure 1 B

my.clusters=as.vector(my.clusters)
index_1=grep("1",my.clusters)
index_2=grep("2",my.clusters)
index_3=grep("3",my.clusters)
index_4=grep("4",my.clusters)
index_5=grep("5",my.clusters)

condition_1=Condition[index_1]
condition_1_ci=grep("Cell competition OFF",condition_1)
condition_1_dmso=grep("Cell competition ON",condition_1)
index_1_fine=c(condition_1_ci,condition_1_dmso)
medium_1=condition_1[index_1_fine]

condition_2=Condition[index_2]
condition_2_ci=grep("Cell competition OFF",condition_2)
condition_2_dmso=grep("Cell competition ON",condition_2)
index_2_fine=c(condition_2_ci,condition_2_dmso)
medium_2=condition_2[index_2_fine]

condition_3=Condition[index_3]
condition_3_ci=grep("Cell competition OFF",condition_3)
condition_3_dmso=grep("Cell competition ON",condition_3)
index_3_fine=c(condition_3_ci,condition_3_dmso)
medium_3=condition_3[index_3_fine]

condition_4=Condition[index_4]
condition_4_ci=grep("Cell competition OFF",condition_4)
condition_4_dmso=grep("Cell competition ON",condition_4)
index_4_fine=c(condition_4_ci,condition_4_dmso)
medium_4=condition_4[index_4_fine]

condition_5=Condition[index_5]
condition_5_ci=grep("Cell competition OFF",condition_5)
condition_5_dmso=grep("Cell competition ON",condition_5)
index_5_fine=c(condition_5_ci,condition_5_dmso)
medium_5=condition_5[index_5_fine]


good_1=good[index_1]
good_finale_1=good_1[index_1_fine]

good_2=good[index_2]
good_finale_2=good_2[index_2_fine]

good_3=good[index_3]
good_finale_3=good_3[index_3_fine]

good_4=good[index_4]
good_finale_4=good_4[index_4_fine]

good_5=good[index_5]
good_finale_5=good_5[index_5_fine]


cluster_1=my.clusters[index_1]
cluster_finale_1=cluster_1[index_1_fine]

cluster_2=my.clusters[index_2]
cluster_finale_2=cluster_2[index_2_fine]

cluster_3=my.clusters[index_3]
cluster_finale_3=cluster_3[index_3_fine]

cluster_4=my.clusters[index_4]
cluster_finale_4=cluster_4[index_4_fine]

cluster_5=my.clusters[index_5]
cluster_finale_5=cluster_5[index_5_fine]



cluster_finale=c(cluster_finale_1,cluster_finale_3,cluster_finale_4,cluster_finale_2,cluster_finale_5)

good_finale=c(good_finale_1,good_finale_3,good_finale_4,good_finale_2,good_finale_5)
medium_finale=c(medium_1,medium_3,medium_4,medium_2,medium_5)


geni_icb_exe=c(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Cdx2")],nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Bmp4")],nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Elf5")])
geni_icb_epi=c(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Gng3")],nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Slc7a3")],nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Pou5f1")])
geni_icb_ave=c(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Hhex")][1],nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Cer1")],nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Lefty1")])
geni_icb_ve=c(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Gata6")],nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Apob")],nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Afp")])

geni_icb=c(geni_icb_epi,geni_icb_exe,geni_icb_ve)
chosen.exprs_new_icb<-log10(data_tpm+1)[geni_icb,good_finale]
row_nuove<-nome_gene[row.names(chosen.exprs_new_icb),2]

geni_exe<-paste(c("Cdx2","Bmp4","Elf5"),"(ExE)",sep="")
geni_ep<-paste(c("Gng3","Slc7a3","Pou5f1"),"(Epi)",sep="")
geni_ve=paste(c("Gata6","Apob","Afp"),"(VE)",sep="")



row.names(chosen.exprs_new_icb)<-c(geni_ep,geni_exe,geni_ve)


numero_clu=rep(0,length(my.clusters))
numero_clu[cluster_finale==1]="Normal (Winner) Epiblast"
numero_clu[cluster_finale==2]="Extraembryonic Ectoderm"
numero_clu[cluster_finale==3]="Intermediate"
numero_clu[cluster_finale==4]="Loser Epiblast"
numero_clu[cluster_finale==5]="Visceral Endoderm"

#medium_clu=rep(0,length(medium_finale))
#medium_clu[medium_finale=="Cell competition OFF"]="CI-treated"
#medium_clu[medium_finale=="Cell competition ON"]="DMSO-treated"

haCol1 = HeatmapAnnotation(df = data.frame(Cluster = numero_clu
                                          ,Condition = medium_finale)
                               , col = list(Condition = c("Cell competition ON" = "yellow 1", "Cell competition OFF"="green 1"
                                                      
                                                     )
                                           
                                            
                                            ,Cluster = c("Normal (Winner) Epiblast" = "#DD6400"
                                                         ,"Extraembryonic Ectoderm" = "#800080"
                                                         ,"Intermediate"= "#0000FF"
                                                                ,"Loser Epiblast"="#006400"
                                                         ,"Visceral Endoderm"="#56B4E9"
                                                         ,"Losing "="#A9A9A9"
                                                         
                                                         )
                                       )
                               , show_legend = T
            )



ht21 = Heatmap(chosen.exprs_new_icb# heat.vals_Mutant_Paired #
               ,cluster_rows = FALSE
                  , col= colorRamp2(c(0, round(max(chosen.exprs_new_icb))), c("white", "red"))
                  , name = "log 10 norm counts"
                  #, column_title = "Absolute values"
                  #, cluster_columns = as.dendrogram(my.tree)
               ,cluster_columns = F

                  #, cluster_rows =as.dendrogram(as.numeric(row.names(log_data_tpm)[1:10]) )
                  , top_annotation = haCol1
                  , row_names_gp = gpar(fontsize = 8
                                       #,col = geneColors
                   )
                  , show_column_names = F
                  , show_row_names = T
                  )

## Warning: The input is a data frame, convert it to the matrix.

#setwd("/Users/gabriele.lubatti/Desktop/Phd/Cell_Competition/methdo_text_paper")
#pdf("heatmap_no_time.pdf",useDingbats = F)
draw(ht21
    #,column_title = "Absolute values", column_title_side = "top"
)

#dev.off()

Extended Figure 2 C and D

row.names(classe)=good
cells_epi=row.names(classe)[classe$cluster==1|classe$cluster==3|classe$cluster==4]

Cluster_col=classe$cluster

library(ggplot2)

set.seed(1000)
dm=DiffusionMap(dissimilarity)

## Warning in DiffusionMap(dissimilarity): You have 723 genes. Consider passing
## e.g. n_pcs = 50 to speed up computation.

DC1=dm$DC1
DC2=dm$DC2
Cluster=as.factor(Cluster_col)
tre=data.frame(DC1,DC2,Cluster)
dpt <- DPT(dm)

#str(dpt)
#tips(dpt)

plot(dpt,col_by = 'DPT158')

cells_epi=row.names(classe)[classe$cluster==1|classe$cluster==3|classe$cluster==4]
set.seed(100)
dm=DiffusionMap(dissimilarity[cells_epi,cells_epi])

## Warning in DiffusionMap(dissimilarity[cells_epi, cells_epi]): You have 512
## genes. Consider passing e.g. n_pcs = 50 to speed up computation.

DC1=dm$DC1
DC2=dm$DC2
Cluster_col=classe[cells_epi,]$cluster
Cluster=as.factor(Cluster_col)
classe_epi=classe[cells_epi,]
Condition=as.factor(classe_epi$condizione)
tre=data.frame(DC1,DC2,Cluster)


 diffu<-ggplot(tre, aes(x=DC1 , y=DC2 , color= Cluster)) + geom_point()
diffu + scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Normal (winner) Epiblast","Intermediate", "Loser Epiblast"))+labs(x = "DC1",y="DC2") + ggtitle("Diffusion Map")

 diffu<-ggplot(tre, aes(x=DC1 , y=DC2 , color= Condition)) + geom_point()+
   scale_color_manual(values=c("green 1", "yellow 1"))
diffu

dpt <- DPT(dm)

#str(dpt)
#tips(dpt)

plot(dpt, col_by = 'DPT307')

time_all_epi=dpt$DPT307

Figure 2 A and D

library(destiny)
cells_epi_fmk=row.names(classe)[(classe$cluster==1 & classe$condizione=="Cell competition OFF")|(classe$cluster==3 & classe$condizione=="Cell competition OFF")|(classe$cluster==4 & classe$condizione=="Cell competition OFF")]

set.seed(1000)
dm=DiffusionMap(dissimilarity[cells_epi_fmk,cells_epi_fmk])
DC1=dm$DC1
DC2=dm$DC2
Cluster_col=classe[cells_epi_fmk,]$cluster
Cluster=as.factor(Cluster_col)
tre=data.frame(DC1,DC2,Cluster)


 diffu<-ggplot(tre, aes(x=DC1 , y=DC2 , color= Cluster)) + geom_point()

  
diffu + scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Normal (winner) Epiblast","Intermediate", "Loser Epiblast"))+labs(x = "DC1",y="DC2") + ggtitle("Diffusion Map")

Condition_clu=classe[cells_epi_fmk,]$condizione
Condition_clu=as.factor(Condition_clu)
Condition=Condition_clu
tre=data.frame(DC1,DC2,Condition_clu)




 diffu<-ggplot(tre, aes(x=DC1 , y=DC2 , color= Condition)) + geom_point()

 
diffu + scale_color_manual(values=c("green 1", "yellow 1"))+labs(x = "DC1",y="DC2") + ggtitle("Diffusion Map")

dpt <- DPT(dm,tips=1)


#tips(dpt)


plot(dpt, col_by = 'DPT119')

time_fmk_epi=dpt$DPT119

setwd(base_path)
save(time_fmk_epi,file="time_fmk_epi.Rda")

Figure 2 E

cells_epi_fmk=row.names(classe)[(classe$cluster==1 & classe$condizione=="Cell competition OFF")|(classe$cluster==3 & classe$condizione=="Cell competition OFF")|(classe$cluster==4 & classe$condizione=="Cell competition OFF")]
cells_epi_dmso=row.names(classe)[(classe$cluster==1 & classe$condizione=="Cell competition ON")|(classe$cluster==3 & classe$condizione=="Cell competition ON")|(classe$cluster==4 & classe$condizione=="Cell competition ON")]
Cluster_clu_fmk=classe[cells_epi_fmk,]$cluster
Cluster_clu_dmso=classe[cells_epi_dmso,]$cluster
Condition_clu_fmk=classe[cells_epi_fmk,]$condizione
Condition_clu_dmso=classe[cells_epi_dmso,]$condizione

set.seed(1000)
dm=DiffusionMap(dissimilarity[cells_epi_fmk,cells_epi_fmk])
dmp=dissimilarity[cells_epi_dmso,cells_epi_fmk]
pred=dm_predict(dm,dmp)


prima=c(dm$DC1,pred[,1]) 
seconda=c(dm$DC2,pred[,2])
cluster=c(Cluster_clu_fmk,Cluster_clu_dmso)
Condition=c(Condition_clu_fmk,Condition_clu_dmso)
dati=data.frame(prima,seconda,cluster)

diffu<-ggplot(dati, aes(x=prima , y=seconda , color= as.factor(cluster))) + geom_point()
  
diffu + scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Winning Epiblast","Losing Epiblast 2", "Losing Epiblast 1"))+labs(x = "DC1",y="DC2") + ggtitle("Diffusion Map")+labs(col="Cluster")

diffu<-ggplot(dati, aes(x=prima , y=seconda , color= as.factor(Condition))) + geom_point()
  
diffu<-ggplot(dati, aes(x=prima, y=seconda, color= Condition)) + geom_point()

 #pdf("figure_paper_pdf/diffusion_map_epi_fmk_condition.pdf",width=8,height=5) 
diffu + scale_color_manual(values=c("green 1", "yellow 1"))+labs(x = "DC1",y="DC2") + ggtitle("Diffusion Map")+labs(col="Condition")

dpt <- DPT(dm)
#save(dpt,file="dpt.Rda")
#str(dpt)
#tips(dpt)
#plot(dpt)
#plot(dpt,col=Cluster)
#pdf("figure_paper_pdf/diffusion_map_all_epi_pseudo_time.pdf",width=8,height=5)

plot(dpt, col_by = 'DPT119')

time_only_fmk=dpt$DPT119
projection_dist_ten=function (dm, new_dcs = NULL, ..., new_data, verbose = FALSE) 
{
    if (is.null(new_dcs)) 
        new_dcs <- dm_predict(dm, new_data, ..., verbose = verbose)
    else if (!missing(new_data)) 
        stop("only pass one of new_dcs and new_data")
    evs <- eigenvectors(dm)
    nns <- find_knn(evs, 10, query = as.matrix(new_dcs))
    nn_dist <- nns$index[, 1:10]
    nn_dist
}

dist=projection_dist_ten(dm,new_dcs=pred)
k10=matrix(0,ncol=10,nrow = length(cells_epi_dmso))
for( i in 1:length(cells_epi_dmso)){
k10[i,]=time_only_fmk[dist[i,]]
#print(i)
}
time_projec=apply(k10,1,mean)







dfForPlot <- data.frame(clusters = as.character(Cluster_clu_fmk)
                                ,exp=time_only_fmk
                                ,Cluster = as.factor(Cluster_clu_fmk))


ggplot(data =dfForPlot
                    ,aes(x = clusters
                         ,y =  exp
                         ,col = Cluster )) +
                geom_boxplot(width=0.9,alpha=0.8) + 
  
  
                scale_color_manual(breaks = c("1","3","4")
                                   ,labels = c("Normal (winner) Epiblast"
                                              
                                              
                                              
                                              ,"Intermediate"
                                              ,"Loser Epiblast "
                                              
                                              
                                              )
                                              
                                             
                                   ,values=c("#DD6400","#0000FF","#006400" )) +
                
  ylab("Losing Score")+xlab(NULL) + 
  geom_sina()+
  ggtitle("Losing score FMK cells (274)")

dfForPlot <- data.frame(clusters = as.character(Cluster_clu_dmso)
                                ,exp=time_projec
                                ,Cluster = as.factor(Cluster_clu_dmso))

ggplot(data =dfForPlot
                    ,aes(x = clusters
                         ,y =  exp
                         ,col = Cluster )) +
                geom_boxplot(width=0.9,alpha=0.8) + 
  
  
                scale_color_manual(breaks = c("1","3","4")
                                   ,labels = c("Normal (winner) Epiblast"
                                              
                                              
                                              
                                              ,"Intermediate"
                                              ,"Loser Epiblast "
                                              
                                              
                                              )
                                              
                                             
                                   ,values=c("#DD6400","#0000FF","#006400" )) +
                
  ylab("Losing Score")+xlab(NULL) + 
  geom_sina()+ylim(c(0,max(time_only_fmk)))+
  ggtitle("Losing score DMSO cells (238)")

Extended Figure 2 E

cells_epi_fmk=row.names(classe)[(classe$cluster==1 & classe$condizione=="Cell competition OFF")|(classe$cluster==3 & classe$condizione=="Cell competition OFF")|(classe$cluster==4 & classe$condizione=="Cell competition OFF")]

time_all_epi=time_all_epi[which(cells_epi%in%cells_epi_fmk)]

time_fmk_epi_fmk_epiblasto=time_fmk_epi
tempi=data.frame(time_all_epi,time_fmk_epi_fmk_epiblasto)

ggplot(data =tempi
                    ,aes(x = time_all_epi
                         ,y =  time_fmk_epi_fmk_epiblasto
                         )) +
                geom_point()+
xlab("pseudo-time FMK (from all epiblast)")+
  ylab("pseudo-time FMK (from only FMK epiblast)")+ggtitle(paste("Spearman correlation :",round(cor(time_all_epi,time_fmk_epi_fmk_epiblasto, method = "spearman"),3),sep=" "))

Figure 2F

primo=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Klf4")]
secondo=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Klf5")]
terzo=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Fgf5")]
quarto=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Zfp42")]
quinto=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Tdgf1")]
sesto=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Mesp1")]
settimo=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="T")]
ottavo=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Neurod1")]
nono=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Sox1")]
decimo=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Sox17")]
quindici=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Gata6")]
undici=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Sox2")]
dodici=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Pou3f1")] 
tredici=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Tcf7l1")]
quattordici=nome_gene$Gene.stable.ID[which(nome_gene$Gene.name=="Pou5f1")]
geni=c(primo,secondo,undici,dodici,tredici,quattordici,quarto,terzo,quinto,sesto,settimo,ottavo,nono,decimo,quindici)


chosen.exprs_new_icb<-log10(data_tpm+1)[geni,cells_epi_fmk]
#colnames(data_tpm)[order(time_all_tutto)]
new_versione=t(chosen.exprs_new_icb)[order(time_fmk_epi),]
chosen.exprs_new_icb=t(new_versione)
row_nuove<-nome_gene[row.names(chosen.exprs_new_icb),2]
#row.names(chosen.exprs_new_icb)<-row_nuove
geni_naive<-paste(c("Klf4*","Klf5*"),"(Naive)",sep="")
geni_epi<-paste(c("Fgf5*","Tdgf1*"),"(Epiblast)",sep="")
#geni_ave<-paste(c("Hhex","Cer1","Lefty1"),"(AVE)",sep="")
geni_meso=paste(c("Mesp1","T"),"(Mesoderm)",sep="")
geni_neuro=paste(c("Neurod1*","Sox1"),"(Neuroderm)",sep="")
geni_endo=paste(c("Sox17*","Gata6"),"(Endoderm)",sep="")
geni_new=paste(c("Sox2","Pou3f1","Tcf7l1*","Pou5f1*","Rex1"),"(Naive)",sep="")




row.names(chosen.exprs_new_icb)<-c(geni_naive,geni_new,geni_epi,geni_meso,geni_neuro,geni_endo)
classe_fmk=meta_dati_course[cells_epi_fmk,]
classe_sort=classe_fmk[order(time_fmk_epi),]
new_clusters=classe_sort$cluster

Condition_new=as.vector(classe_sort$condition)

numero_clu=rep(0,length(new_clusters))
numero_clu[new_clusters==1]="Normal (Winner) Epiblast"
numero_clu[new_clusters==2]="Extraembryonic Ectoderm"
numero_clu[new_clusters==3]="Intermediate"
numero_clu[new_clusters==4]="Loser Epiblast"
numero_clu[new_clusters==5]="Visceral Endoderm"
numero_clu[new_clusters==6]="Losing "



bo=data.frame(Condition = Condition_new,Cluster = numero_clu,
                                          Pseudotime=sort(time_fmk_epi))

haCol1 = HeatmapAnnotation(df = data.frame(Condition = Condition_new
                                          ,Cluster = numero_clu,
                                          Pseudotime=sort(time_fmk_epi))
                               , col = list(Condition = c("Cell competition ON" = "yellow 1", "Cell competition OFF"="green 1"
                                                      
                                                     )
                                           
                                            
                                            ,Cluster = c("Normal (Winner) Epiblast" = "#DD6400"
                                                         ,"Extraembryonic Ectoderm" = "#800080"
                                                         ,"Intermediate"= "#0000FF"
                                                                ,"Loser Epiblast"="#006400"
                                                         ,"Visceral Endoderm"="#56B4E9"
                                                         ,"Losing "="#A9A9A9"
                                                         
                                                         )
                                       ,Pseudotime=colorRamp2(c(0, round(max(time_fmk_epi))), c("white", "blue")))
                               , show_legend = T
            )



#pdf("figure_paper_pdf/heatmap_pseudo_time_naive.pdf",width=8,height=5)




ht21 = Heatmap(chosen.exprs_new_icb# heat.vals_Mutant_Paired #
               ,cluster_rows = FALSE
                  , col= colorRamp2(c(0, round(max(chosen.exprs_new_icb))), c("white", "red"))
                  , name = "log 10 norm counts"
                  #, column_title = "Absolute values"
                  , cluster_columns = F
,column_title = " Naive and primitive streak markers"
                  #, cluster_rows =as.dendrogram(as.numeric(row.names(log_data_tpm)[1:10]) )
                  , top_annotation = haCol1
                  , row_names_gp = gpar(fontsize = 8
                                       #,col = geneColors
                   )
                  , show_column_names = F
                  , show_row_names = T
                  )



draw(ht21
    #,column_title = "Absolute values", column_title_side = "top"
)

Extended Figure 3 D

singolo_correlazione=function(a,b,c,d){
classe_prova=classe[grep(b,classe$Cell_type),]
classe_los=classe_prova[grep(a,classe_prova$cluster),]
exp_1_los=t((data_tpm))[rownames(classe_los),nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==c)]]
exp_1_los=as.vector(exp_1_los)
exp_1_los_1=t((data_tpm))[rownames(classe_los),nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==d)]]
exp_1_los_1=as.vector(exp_1_los_1)
dati=data.frame(exp_1_los,exp_1_los_1)
p=ggplot(data=dati,aes(x=exp_1_los_1,y=exp_1_los))+geom_point(col="#006400")+xlab(d)+
  ylab(c)+ggtitle(paste(c,"vs",d,"-spearman corr:",round(cor(exp_1_los_1, exp_1_los, method = "spearman"),3),sep=" "))
p
}

singolo_correlazione("4","FMK","Klf4","Sox17")

singolo_correlazione("4","FMK","Klf4","Neurod1")

singolo_correlazione("4","FMK","Neurod1","Sox17")

GAM Model for detecting DE genes along the pseudo time using only Cell Competition OFF epiblast cells

row.names(classe)=good
cellule_epi_fmk=row.names(classe)[(classe$cluster==1 & classe$condizione=="Cell competition OFF")|(classe$cluster==3 & classe$condizione=="Cell competition OFF")|(classe$cluster==4 & classe$condizione=="Cell competition OFF")]
cellule_epi=row.names(classe)[(classe$cluster==1)|(classe$cluster==3) |(classe$cluster==4 )]

data=data_tpm
data=data[,cellule_epi_fmk]
risultato=apply(data,1,function(x){sum(x>15)})
indice=which(as.vector(risultato)>10)
background_trajectory=row.names(data_tpm)[indice]

data=data[indice,]
data=log10(data+1)
sce<-SingleCellExperiment(assays = list(counts = as.matrix(data)))
sce$Pseudotime <- time_fmk_epi
Y=counts(sce)
#Y_1=Y[1:2,]
t=sce$Pseudotime

gam.res <- apply(Y, 1, function(z){
    d <- data.frame(z=z, t=t)
    tmp <- gam(z ~ lo(t), data=d)
    p <- summary(tmp)[4][[1]][1,5]  #p-value parametric ANOVA
    f<-fitted(tmp)
    #p2 <- summary(tmp)[3][[1]][2,3]  #p-value non-parametric ANOVA
    c(p,f)
    #c(p1,p2)   #If we want to return both the parametric and non-parametric p-values
})
data_all=data


#d <- data.frame(z=Y[1,], t=t)
    #tmp <- gam(z ~ lo(t), data=d)
    #p <- summary(tmp)[4][[1]][1,5]  #p-value parametric ANOVA
    #f<-fitted(tmp)

Keeping only the genes which vary in a significant way along the pseudo time

genes.table<-data.frame(genes.names=rownames(data))
genes.table$pvals<-gam.res[1,]
genes.table$FDR<-p.adjust(genes.table$pvals, method="fdr") #Adjust p-values
genes.table<-genes.table[order(genes.table$FDR),] #Order the genes according to the FDR
genes.table$genes.names <- as.character(genes.table$genes.names)

#Note that we are using a very strict threshold on FDR! Otherwise we obtain a very large number
#of genes
results.gam.tot<-genes.table[genes.table$FDR < 0.01,][c('genes.names','FDR')] #Filter the significant genes

#Save the matrix of fitted values
gam.fitted<-gam.res[-1,]


print(paste("We have"
           ,sum(genes.table$FDR < 0.01)
           ,"significant genes"))

## [1] "We have 5310 significant genes"

#results_gam=results.gam.tot['genes.names']
results_gam_nomi=results.gam.tot$genes.names
gam_fitted=gam.fitted

data=data[results_gam_nomi,]

data=t(data)
data_100_sort=data

Hierarchical cluster analysis for detecting genes who behave in a similar way along the trajectory

chosen.exprs1<-as.matrix(data_100_sort)
dissimilarity <- sqrt((1-cor((chosen.exprs1), method = "spearman"))/2)

#Umap by medium
my.dist <- as.dist(dissimilarity) 
set.seed(100)
#object<-umap(d=as.matrix(my.dist),input="dist")
#save(object,file="object_novembre.Rda")

object<-umap(d=as.matrix(my.dist),input="dist")
umap_met<-as.data.frame(object$layout)
#umap_met


my.tree <- hclust(my.dist
                  , method="ward.D2" # met = "ward.D2" was adopted form the reference
                  )
# Cut the treee to identify clusters
#save(my.tree,file="my_tree_novembre.Rda")
my.clusters <- cutreeHybrid(my.tree
                            ,distM=as.matrix(my.dist),deepSplit=0
                            ,minClusterSize = 100)$label#5)$label

##  ..cutHeight not given, setting it to 14.3  ===>  99% of the (truncated) height range in dendro.
##  ..done.

#save(my.clusters,file="my_clusters_novembre.Rda")
#length(unique(my.clusters))
#classe<-data.frame(Cell_type = meta_dati_no$Sample
                                          #,cluster = my.clusters)

#classe$condizione=Condition 
#table(classe$cluster,classe$condizione)
Cluster=as.factor(my.clusters)
sp<-ggplot(as.data.frame(object$layout),aes(umap_met[,1],umap_met[,2],color=Cluster)) + geom_point()+xlab("Umap 1")+ylab("Umap 2")+ ggtitle("Cluster on top (FDR<0.01)  varying genes along pseudotime")

sp

Identification of DE genes (up= increasing along the trajectory, down= decreasing along the trajectory)

classe_geni=data.frame(colnames(data_100_sort),my.clusters)

colnames(classe_geni)=c("geni","clusters")
row.names(classe_geni)=classe_geni$geni

geni_1=nome_gene[(classe_geni[classe_geni$clusters==1,]$geni),2]
geni_2=nome_gene[(classe_geni[classe_geni$clusters==2,]$geni),2]
geni_3=nome_gene[(classe_geni[classe_geni$clusters==3,]$geni),2]
geni_4=nome_gene[(classe_geni[classe_geni$clusters==4,]$geni),2]


id_1=as.vector(classe_geni$geni[classe_geni$clusters==1])
id_2=as.vector(classe_geni$geni[classe_geni$clusters==2])
id_3=as.vector(classe_geni$geni[classe_geni$clusters==3])
id_4=as.vector(classe_geni$geni[classe_geni$clusters==4])

id_down_loser=c(id_1,id_2,id_4)
id_up_loser=c(id_3)

row.names(results.gam.tot)=results.gam.tot$genes.names
id_1_full=results.gam.tot[id_1,]
id_1_full$Cluster=classe_geni[id_1,]$clusters
id_2_full=results.gam.tot[id_2,]
id_2_full$Cluster=classe_geni[id_2,]$clusters
id_3_full=results.gam.tot[id_3,]
id_3_full$Cluster=classe_geni[id_3,]$clusters
id_4_full=results.gam.tot[id_4,]
id_4_full$Cluster=classe_geni[id_4,]$clusters
id_down_full=results.gam.tot[id_down_loser,]
id_down_full$Cluster=classe_geni[id_down_loser,]$clusters

id_down_full_sort=id_down_full[order(id_down_full$FDR),]
id_down_full_sort$Cluster=classe_geni[row.names(id_down_full_sort),]$clusters

Extended Figure 3 A

row.names(classe)=good
cellule_epi_fmk=row.names(classe)[(classe$cluster==1 & classe$condizione=="Cell competition OFF")|(classe$cluster==3 & classe$condizione=="Cell competition OFF")|(classe$cluster==4 & classe$condizione=="Cell competition OFF")]

geni_up=results_gam_nomi[results_gam_nomi%in%id_up_loser]
geni_down=results_gam_nomi[results_gam_nomi%in%id_down_loser]
geni=c(geni_up,geni_down)
geni_colore=rep(0,length(geni))
geni_colore[1:length(geni_up)]="Increasing"
geni_colore[(length(geni_up)+1):length(geni)]="Decreasing"
DE_genes=geni_colore

chosen.exprs_new_icb<-log10(data_tpm+1)[geni,cellule_epi_fmk]

new_versione=t(chosen.exprs_new_icb)[order(time_fmk_epi),]
chosen.exprs_new_icb=t(new_versione)
row_nuove<-nome_gene[row.names(chosen.exprs_new_icb),2]


geni_nomi=nome_gene[geni,]$Gene.name



row.names(chosen.exprs_new_icb)<-geni_nomi
row.names(classe)=good
classe_fmk=classe[cellule_epi_fmk,]
classe_sort=classe_fmk[order(time_fmk_epi),]
new_clusters=classe_sort$cluster

Condition_new=classe_sort$condizione

numero_clu=rep(0,length(new_clusters))
numero_clu[new_clusters==1]="Normal (Winner) Epiblast"
numero_clu[new_clusters==2]="Extraembryonic Ectoderm"
numero_clu[new_clusters==3]="Intermediate"
numero_clu[new_clusters==4]="Loser Epiblast"
numero_clu[new_clusters==5]="Visceral Endoderm"
numero_clu[new_clusters==6]="Losing "

haCol1 = HeatmapAnnotation(df = data.frame(
                                          Cluster = numero_clu,
                                          Condition = Condition_new,
                                          Pseudotime=sort(time_fmk_epi))
                               , col = list(Condition = c("Cell competition ON" = "yellow 1", "Cell competition OFF"="green 1"
                                                      
                                                     )
                                           
                                            
                                            ,Cluster = c("Normal (Winner) Epiblast" = "#DD6400"
                                                         ,"Extraembryonic Ectoderm" = "#800080"
                                                         ,"Intermediate"= "#0000FF"
                                                                ,"Loser Epiblast"="#006400"
                                                         ,"Visceral Endoderm"="#56B4E9"
                                                         ,"Losing "="#A9A9A9"
                                                         
                                                         )
                                       ,Pseudotime=colorRamp2(c(0, round(max(time_fmk_epi))), c("white", "blue")))
                               , show_legend = T,which = c("column")
            )


left_annotation=HeatmapAnnotation(df = data.frame(
                                          DE_genes=DE_genes)
                               , col = list(DE_genes = c("Increasing" = "black", "Decreasing"="grey")
                                                      
                                                     )
                                           
                                            
                                            
                               , show_legend = T,which = c("row")
            )








ht21 = Heatmap(chosen.exprs_new_icb# heat.vals_Mutant_Paired #
               ,cluster_rows = FALSE
                  , col= colorRamp2(c(0,round(max(chosen.exprs_new_icb))), c("blue","red"))
                  , name = "log 10 norm counts"
                  #, column_title = "Absolute values"
                  , cluster_columns = F
,column_title = "Diff expressed genes"
                  #, cluster_rows =as.dendrogram(as.numeric(row.names(log_data_tpm)[1:10]) )
                  , top_annotation = haCol1
                  , row_names_gp = gpar(fontsize = 8
                                       #,col = geneColors
                   )
                  , show_column_names = F
                  , show_row_names = F
         ,left_annotation = left_annotation  )

## `use_raster` is automatically set to TRUE for a matrix with more than
## 2000 rows. You can control `use_raster` arugment by explicitly setting
## TRUE/FALSE to it. Set `ht_opt$message = FALSE` to turn off this
## message.

draw(ht21
    #,column_title = "Absolute values", column_title_side = "top"
)

Extended Figure 3 B and C

up_loser_1_4=data.frame(id_up_loser)
down_loser_1_4=data.frame(id_down_loser)
row.names(up_loser_1_4)=id_up_loser
row.names(down_loser_1_4)=id_down_loser





target_up=data_p53$Ensembl.ID[grep("up",data_p53$Literature..Table.S1.)]
target_down=data_p53$Ensembl.ID[grep("down",data_p53$Literature..Table.S1.)]
#write.csv2(target_up,file="target_up_p53.csv")
#write.csv2(target_down,file="target_down_p53.csv")
#ho generato i seguenti due file con g profiler
#up_p53_mouse=read.csv("up_p53_topo.csv",sep=",",header=T)
#down_p53_mouse=read.csv("down_p53_topo.csv",sep=",",header=T)
#test up
test=up_p53_mouse$initial_ensg

test= !(duplicated(test) | duplicated(test, fromLast = TRUE)) # correct result

up_p53=up_p53_mouse$ortholog_ensg[test]

up_p53=up_p53[up_p53%in%row.names(down_loser_1_4)|up_p53%in%row.names(up_loser_1_4)]

sum(up_p53%in%row.names(up_loser_1_4))/(length(up_p53))

## [1] 0.5625

sum(up_p53%in%row.names(down_loser_1_4))/(length(up_p53))

## [1] 0.4375

#test down
test=down_p53_mouse$initial_ensg

test= !(duplicated(test) | duplicated(test, fromLast = TRUE)) # correct result

down_p53=down_p53_mouse$ortholog_ensg[test]

down_p53=down_p53[down_p53%in%row.names(down_loser_1_4)|down_p53%in%row.names(up_loser_1_4)]

sum(down_p53%in%row.names(up_loser_1_4))/(length(down_p53))

## [1] 0.195122

sum(down_p53%in%row.names(down_loser_1_4))/(length(down_p53))

## [1] 0.804878

tot_p53=c(up_p53,down_p53)


matrice=matrix(c(sum(up_p53%in%row.names(up_loser_1_4)),length(up_p53)-sum(up_p53%in%row.names(up_loser_1_4)),sum(down_p53%in%row.names(up_loser_1_4)),sum(down_p53%in%row.names(down_loser_1_4))),nrow=2,ncol=2,byrow=T)
#Fisher test
fisher.test(matrice)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  matrice
## p-value = 0.0001076
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   2.046214 14.816715
## sample estimates:
## odds ratio 
##   5.229645

matrice_1=matrix(c(sum(down_p53%in%row.names(down_loser_1_4)),length(down_p53)-sum(down_p53%in%row.names(down_loser_1_4)),sum(up_p53%in%row.names(down_loser_1_4)),sum(up_p53%in%row.names(up_loser_1_4))),nrow=2,ncol=2,byrow=T)

fisher.test(matrice_1)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  matrice_1
## p-value = 0.0001076
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##   2.046214 14.816715
## sample estimates:
## odds ratio 
##   5.229645

a=rep("Up genes (45)",1)
b=rep("Down genes (35)",1)
aa=sum(up_p53%in%row.names(up_loser_1_4))
bb=sum(up_p53%in%row.names(down_loser_1_4))
value=c(aa,bb)
group=c(a,b)
df=data.frame(group,value)
library(ggplot2)
bp<- ggplot(df, aes(x="", y=value, fill=group))+
  geom_bar(width = 1, stat = "identity")
#bp


pie <- bp + coord_polar("y", start=0)
#pie
library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:viridis':
## 
##     viridis_pal

blank_theme <- theme_minimal()+
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.border = element_blank(),
    panel.grid=element_blank(),
    axis.ticks = element_blank(),
    plot.title=element_text(size=14, face="bold")
  )

pie +  blank_theme +
  theme(axis.text.x=element_blank()) +
  geom_text(aes(y=c(6,60),
                label = percent(value/80)), size=5)+ggtitle("Targets activated by P53")

a=rep("Up genes (8)",1)
b=rep("Down genes (33)",1)
aa=sum(down_p53%in%row.names(up_loser_1_4))
bb=sum(down_p53%in%row.names(down_loser_1_4))
value=c(aa,bb)
group=c(a,b)
df=data.frame(group,value)
library(ggplot2)
bp<- ggplot(df, aes(x="", y=value, fill=group))+
  geom_bar(width = 1, stat = "identity")
#bp


pie <- bp + coord_polar("y", start=0)
#pie
library(scales)

blank_theme <- theme_minimal()+
  theme(
    axis.title.x = element_blank(),
    axis.title.y = element_blank(),
    panel.border = element_blank(),
    panel.grid=element_blank(),
    axis.ticks = element_blank(),
    plot.title=element_text(size=14, face="bold")
  )

pie +  blank_theme +
  theme(axis.text.x=element_blank()) +
  geom_text(aes(y=c(4,30),
                label = percent(value/41)), size=5)+ggtitle("Targets repressed by P53")

gene_expression <- function(a,b){
  #row.names(clas_new_new)=good
 
  geni_up_loser=row.names(classe_geni)[classe_geni$clusters==3]
geni_down_loser=row.names(classe_geni)[classe_geni$clusters==1|classe_geni$clusters==2|classe_geni$clusters==4]

  classe_new=classe[classe$cluster==1|classe$cluster==3|classe$cluster==4,]
clas_new_new=classe_new
clas_new_new=clas_new_new[grep("FMK",classe_new$Cell_type),]
classe=clas_new_new

  
  exp_real=t(log10(data_tpm+1))[rownames(classe),nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]]
  #exp_fitted=gam.fitted[rownames(classe),which(colnames(gam.fitted)==nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)])]
  #exp=c(exp_real)
pseudo_time_real=time_fmk_epi
#pseudo_time_fit=time_all
#type=c(rep("real expression",length(exp_real)),rep("fitted value",length(exp_real)))
#time_all_all=c(pseudo_time_real,pseudo_time_fit)
dfForPlot <- data.frame(pseudo_time = pseudo_time_real
                                ,genes_expression=exp_real
                        
)
if(sum(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%geni_up_loser)>0){
  titolo=paste(a,"up_loser",b,sep=" ")
}
if(sum(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%geni_down_loser)>0){
  titolo=paste(a,"down_loser",b,sep=" ")
}

if(sum(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%colnames(gam.fitted))==0){titolo=paste(a,"no in the background",b,sep=" ")}
if(sum((nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%colnames(gam.fitted)>0)&(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%row.names(classe_geni)==0))){titolo=paste(a,"no up/down",b,sep=" ")}

if(sum(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%colnames(gam.fitted))==0){titolo=paste(a,"no in the background",b,sep=" ")}

p <- ggplot(data =dfForPlot
                    ,aes(x = pseudo_time
                         ,y =  genes_expression
                         
                          )) +
                 geom_point()+xlab("pseudotime")+ylab("log 10 norm counts")+
  ggtitle(titolo)+geom_smooth(method = "loess", se = F,col="red") 
  
  
p}

Figure 3 C and Extended Figure 4 D

p_12=gene_expression("mt-Atp6"," ")
p_13=gene_expression("mt-Nd3"," ")
p_14=gene_expression("Opa1"," ")
p_15=gene_expression("Samm50"," ")
#pdf("figure_paper_pdf/pseudo_time_elctron.pdf",width=8,height=5)

grid.arrange(p_12,p_13,top=textGrob("Electron Transport chain",gp=gpar(fontsize=15)),ncol=1,nrow=2)

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

grid.arrange(p_14,p_15,top=textGrob("Mitochondria Membrane",gp=gpar(fontsize=15)),ncol=1,nrow=2)

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

p_12=gene_expression("Atf4"," ")
p_13=gene_expression("Ddit3"," ")
p_14=gene_expression("Foxo3"," ")
p_15=gene_expression("Nfe2l2"," ")
p_16=gene_expression("Sesn2"," ")

grid.arrange(p_14,p_15,ncol=1,nrow=2)

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

grid.arrange(p_12,p_13,ncol=1,nrow=2)

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

grid.arrange(p_16,ncol=1,nrow=1)

## `geom_smooth()` using formula 'y ~ x'

gene_expression <- function(a,b){
  #row.names(clas_new_new)=good
 
  geni_up_loser=row.names(classe_geni)[classe_geni$clusters==3]
geni_down_loser=row.names(classe_geni)[classe_geni$clusters==1|classe_geni$clusters==2|classe_geni$clusters==4]

  classe_new=classe[classe$cluster==1|classe$cluster==3|classe$cluster==4,]
clas_new_new=classe_new
clas_new_new=clas_new_new[grep("FMK",classe_new$Cell_type),]
classe=clas_new_new
cluster_plot=classe[,2]
cluster_plot[cluster_plot==1]="Winner Epiblast"
cluster_plot[cluster_plot==3]="Intermediate"
cluster_plot[cluster_plot==4]="Loser Epiblast"
cluster_plot=factor(cluster_plot,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"))
  
  exp_real=t(log10(data_tpm+1))[rownames(classe),nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]]
  #exp_fitted=gam.fitted[rownames(classe),which(colnames(gam.fitted)==nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)])]
  #exp=c(exp_real)
pseudo_time_real=time_fmk_epi
#pseudo_time_fit=time_all
#type=c(rep("real expression",length(exp_real)),rep("fitted value",length(exp_real)))
#time_all_all=c(pseudo_time_real,pseudo_time_fit)
dfForPlot <- data.frame(pseudo_time = pseudo_time_real
                                ,genes_expression=exp_real
                        ,cluster=cluster_plot
                        
)
if(sum(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%geni_up_loser)>0){
  titolo=paste(a,"up_loser",b,sep=" ")
}
if(sum(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%geni_down_loser)>0){
  titolo=paste(a,"down_loser",b,sep=" ")
}

if(sum(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%colnames(gam.fitted))==0){titolo=paste(a,"no in the background",b,sep=" ")}
if(sum((nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%colnames(gam.fitted)>0)&(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%row.names(classe_geni)==0))){titolo=paste(a,"no up/down",b,sep=" ")}

if(sum(nome_gene$Gene.stable.ID[which(nome_gene$Gene.name==a)]%in%colnames(gam.fitted))==0){titolo=paste(a,"no in the background",b,sep=" ")}

p <- ggplot(data =dfForPlot
                    ,aes(x = pseudo_time
                         ,y =  genes_expression
                         ,color=(cluster_plot)
                          )) +
                 geom_point()+xlab("pseudotime")+ylab("log 10 norm counts")+
  ggtitle(titolo)+geom_smooth(method = "loess", se = F,col="red") +scale_color_manual(values=c("#DD6400", "#0000FF", "#006400"))+labs(col="Cluster")
  
p <- ggplot(data =dfForPlot
                    ,aes(x = cluster_plot
                         ,y =  genes_expression
                         ,colour=(cluster_plot)
                          )) +
                 geom_boxplot()+xlab("Cluster")+ylab("log 10 norm counts")+
  ggtitle(titolo) +scale_colour_manual(values=c("#DD6400", "#0000FF", "#006400"))+labs(colour="Cluster")
  
  
list(p,q)}

#("Normal (Winner) Epiblast" = "#DD6400"
                                                        # ,"Extraembryonic Ectoderm" = "#800080"
                                                         #,"Intermediate"= "#0000FF"
                                                            #    ,"Loser Epiblast"="#006400"
                                                        # ,"Visceral Endoderm"="#56B4E9"
                                                        # ,"Losing "="#A9A9A9"

#gene_expression("Tfb1m"," ")

#setwd("/Users/gabriele.lubatti/Desktop")
#geni_upr=read.csv2("ana_upr.csv",header=F)
geni_upr=as.vector(geni_upr$V1)

geni_down=nome_gene[id_down_loser,]$Gene.name
geni_up=nome_gene[id_up_loser,]$Gene.name

geni_upr[which(!(geni_upr%in%nome_gene$Gene.name))]="Foxo3"

Extended Figure 4 A

library(UpSetR)
up=geni_up
down=geni_down
upr=geni_upr

listInput <- list(up_loser = up, down_loser =down,UPR_ISR = upr) 
upset(fromList(listInput), order.by = "freq")

back_nomi=nome_gene[background_trajectory,]$Gene.name

Extended Figure 4 B and C

geni_up=nome_gene[row.names(id_3_full),]$Gene.name
id_3_full$nomi=geni_up

geni_up_com=geni_up[which(geni_up%in%geni_upr)]
index_up_com=which(geni_up%in%geni_upr)
fdr_up_com=id_3_full[which(geni_up%in%geni_upr),]$FDR
up_com_1=data.frame(geni_up_com,fdr_up_com,index_up_com)

up_com_1

##   geni_up_com   fdr_up_com index_up_com
## 1       Ddit3 4.746395e-39            2
## 2        Atf3 6.102567e-27           22
## 3        Atf4 2.201811e-23           31
## 4       Foxo3 2.738259e-22           37
## 5    Ppp1r15a 8.494053e-18           68
## 6     Eif2ak3 7.200497e-13          150
## 7      Nfe2l2 1.563468e-10          207
## 8       Gdf15 5.512919e-08          331

geni_down=nome_gene[row.names(id_down_full_sort),]$Gene.name
id_down_full_sort=id_down_full_sort[,-1]
id_down_full_sort$nomi=geni_down

geni_down_com=geni_down[which(geni_down%in%geni_upr)]
index_down_com=which(geni_down%in%geni_upr)
fdr_down_com=id_down_full_sort[which(geni_down%in%geni_upr),]$FDR
down_com_1=data.frame(geni_down_com,fdr_down_com,index_down_com)


down_com_1

##    geni_down_com fdr_down_com index_down_com
## 1        Mthfd1l 2.504068e-35            147
## 2          Hspe1 8.853980e-34            164
## 3            Cat 2.428744e-30            219
## 4          Hspd1 6.863457e-13           1262
## 5           Sod2 1.255766e-10           1552
## 6          Hsph1 4.447044e-10           1654
## 7          Lonp1 1.061746e-06           2346
## 8          Eif2a 1.469514e-06           2381
## 9         Mthfd2 1.297814e-05           2691
## 10         Hspa4 2.819542e-05           2789
## 11           Cth 2.533223e-03           3677
## 12          Nrf1 2.843621e-03           3695

back=background_trajectory
#versione corretta con geni id 
id_geni_upr=nome_gene$Gene.stable.ID[nome_gene$Gene.name%in%geni_upr]


#arricchimento per up

matrice=matrix(c(sum(id_up_loser%in%id_geni_upr),sum(!id_up_loser%in%id_geni_upr),sum(back[!(back%in%id_up_loser)]%in%id_geni_upr),sum(!(back[!(back%in%id_up_loser)]%in%id_geni_upr))),nrow=2,ncol=2,byrow = T)
fisher.test(matrice)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  matrice
## p-value = 0.01271
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  1.148861 7.414376
## sample estimates:
## odds ratio 
##    3.06029

#arricchimento per down
matrice=matrix(c(sum(id_down_loser%in%id_geni_upr),sum(!(id_down_loser%in%id_geni_upr)),sum(back[!(back%in%id_down_loser)]%in%id_geni_upr),sum(!(back[!(back%in%id_down_loser)]%in%id_geni_upr))),nrow=2,ncol=2,byrow = T)
fisher.test(matrice)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  matrice
## p-value = 0.6902
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.524691 2.899567
## sample estimates:
## odds ratio 
##   1.243201

setwd(base_path)
raw_dati=read.table("GeneCounts.csv",sep=",",header=T)
row.names(raw_dati)=raw_dati$X
raw_dati=raw_dati[,-1]
colnames(raw_dati)[9]="P2A4HB_GFP_CO_1"
colnames(raw_dati)[10]="P2A4HB_GFP_CO_2"



setwd(base_path)
meta_data=read.table("metadata_ana_new.csv",sep=";",header=F)
nuovo=as.vector(meta_data$V5)
nuovo[meta_data$V5=="Separate"]="Sep"
nuovo[meta_data$V5=="Co-culture"]="CO"
meta_data$V5=nuovo
nomi_colonne=paste(meta_data$V4,meta_data$V5,meta_data$V6,sep="_")

nomi_colonne[which(!nomi_colonne%in%colnames(raw_dati))]=c("P2A4HB_GFP_Sep_1", "P2A4HB_GFP_CO_1", "P2A4HB_GFP_Sep_2", "P2A4HB_GFP_CO_2","P2A4HB_GFP_Sep_3", "P2A4HB_GFP_CO_3",  "P2A4HB_GFP_Sep_4", "P2A4HB_GFP_CO_4" )

nuovi_dati=raw_dati[,nomi_colonne]
colnames(nuovi_dati)=as.vector(meta_data$V3)

De_edger=function(Treat,Data){
Treat=relevel(Treat, ref="Win_Epi")
y=DGEList(counts=Data, group=Treat)
keep <- filterByExpr(y)
tutto=keep
table(keep)
y=y[keep, , keep.lib.sizes=FALSE]
y=calcNormFactors(y)
#y$samples
design <- model.matrix(~Treat)
rownames(design) <- colnames(y)
#design
y=estimateDisp(y, design, robust=TRUE)
#y$common.dispersion
fit=glmQLFit(y, design, robust=TRUE)
#qlf=glmQLFTest(fit, coef=1:5)
#qlf=glmLRT(fit)
#topTags(qlf)
#FDR= p.adjust(qlf$table$PValue, method="BH")
#qlf$table<-cbind(qlf$table,FDR)
#sum(FDR < 0.05)
qlf=glmQLFTest(fit)
FDR= p.adjust(qlf$table$PValue, method="BH")
qlf$table<-cbind(qlf$table,FDR)
sum(FDR < 0.05)
topTags(qlf)
summary(decideTests(qlf))
#plotMD(qlf)
#abline(h=c(-1,1), col="blue")
tr_new<-qlf$table[row.names(qlf$table)[qlf$table$FDR<0.001 & !is.na(qlf$table$FDR)],]
#return(list(tr_new))
#}

tr_export_down<-tr_new[row.names(tr_new)[tr_new$logFC<0],]

tr_down<-tr_export_down[order(tr_export_down$FDR),]
tr_down_1_4_new<-tr_down

tr_export_up<-tr_new[row.names(tr_new)[tr_new$logFC>0],]
tr_up<-tr_export_up[order(tr_export_up$FDR),]
tr_up_1_4_new<-tr_up
name_up=nome_gene[row.names(tr_up_1_4_new),2]
tr_up_1_4_new$name=name_up

name_down=nome_gene[row.names(tr_down_1_4_new),2]
tr_down_1_4_new$name=name_down
return(list(tr_up_1_4_new,tr_down_1_4_new,tutto))
}

#prima funzione
colnames(raw_dati)[9]="P2A4HB_GFP_CO_1"
colnames(raw_dati)[10]="P2A4HB_GFP_CO_2"
winner=colnames(raw_dati)[grep("P2A4HB_GFP_CO",colnames(raw_dati))]
loser=colnames(raw_dati)[grep("P1A3BG_CO",colnames(raw_dati))]
cel=c(winner,loser)
data_de=raw_dati[,cel]
tre_1=(rep("Win_Epi",length(winner)))
tre_2=(rep("Los_Epi",length(loser)))
Treat=c(tre_1,tre_2)
Treat=as.factor(Treat)
#Time=as.vector((classe[cel,]$Cell_type))
#Time=as.factor(Time)
Output_De=De_edger(Treat,data_de)
up_1_4=Output_De[[1]]
down_1_4=Output_De[[2]]
tutto=Output_De[[3]]
background_all=row.names(raw_dati)[tutto]


#id_down_loser
#id_up_loser



back_all=intersect(background_trajectory,background_all)

Extended Figure 10 B

library(UpSetR)
up_1_3_n=id_up_loser
down_1_3_n=id_down_loser
up_1_4_n=row.names(up_1_4)
down_1_4_n=row.names(down_1_4)
listInput <- list(up_vivo = up_1_3_n, down_vivo =down_1_3_n, up_vitro = up_1_4_n,down_vitro=down_1_4_n) 
upset(fromList(listInput), order.by = "freq")

down_1_4=down_1_4[intersect(row.names(down_1_4),back_all),]
up_1_4=up_1_4[intersect(row.names(up_1_4),back_all),]
id_up_loser=intersect(id_up_loser,back_all)
id_down_loser=intersect(id_down_loser,back_all)

#arrichimento per down
matrice=matrix(c(sum(row.names(down_1_4)%in%id_down_loser),sum(row.names(down_1_4)%in%back_all)-sum(row.names(down_1_4)%in%id_down_loser),sum(id_down_loser%in%back_all)-sum(row.names(down_1_4)%in%id_down_loser),sum(!(back_all%in%row.names(down_1_4))&!(back_all%in%id_down_loser))),nrow=2,ncol=2,byrow = T)
#Fisher test
fisher.test(matrice)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  matrice
## p-value = 1.706e-12
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  1.523194 2.128908
## sample estimates:
## odds ratio 
##   1.799954

#arrichimento per up
matrice=matrix(c(sum(row.names(up_1_4)%in%id_up_loser),sum(row.names(up_1_4)%in%back_all)-sum(row.names(up_1_4)%in%id_up_loser),sum(id_up_loser%in%back_all)-sum(row.names(up_1_4)%in%id_up_loser),sum(!(back_all%in%row.names(up_1_4))&!(back_all%in%id_up_loser))),nrow=2,ncol=2,byrow = T)
#Fisher test
fisher.test(matrice)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  matrice
## p-value = 0.3314
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.8780489 1.4101126
## sample estimates:
## odds ratio 
##   1.117812

#arrichimento per up e down
matrice=matrix(c(sum(row.names(up_1_4)%in%id_down_loser),sum(row.names(up_1_4)%in%back_all)-sum(row.names(up_1_4)%in%id_down_loser),sum(id_down_loser%in%back_all)-sum(row.names(up_1_4)%in%id_down_loser),sum(!(back_all%in%row.names(up_1_4))&!(back_all%in%id_down_loser))),nrow=2,ncol=2,byrow = T)
#Fisher test
fisher.test(matrice)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  matrice
## p-value = 0.00487
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.6801619 0.9348872
## sample estimates:
## odds ratio 
##  0.7979219

#arrichimmento per down e up
matrice=matrix(c(sum(row.names(down_1_4)%in%id_up_loser),sum(row.names(down_1_4)%in%back_all)-sum(row.names(down_1_4)%in%id_up_loser),sum(id_up_loser%in%back_all)-sum(row.names(down_1_4)%in%id_up_loser),sum(!(back_all%in%row.names(down_1_4))&!(back_all%in%id_up_loser))),nrow=2,ncol=2,byrow = T)
#Fisher test
fisher.test(matrice)

## 
##  Fisher's Exact Test for Count Data
## 
## data:  matrice
## p-value = 0.006239
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
##  0.4867568 0.8964258
## sample estimates:
## odds ratio 
##  0.6667054

setwd(base_path)
ward_down_no=read.csv("down_ana_last_version.csv")
#go_up_34_function=go_up_34[go_up_34$source=="GO:MF",]
#up_34_function=as.vector(go_up_34_function$term_name)
#go_up_34_process=go_up_34[go_up_34$source=="GO:BP",]
#up_34_process=as.vector(go_up_34_process$term_name)

ward_down_component=ward_down_no[ward_down_no$source=="WP",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)

down_cluster_all_function=down_output_no#tabella da vedere

ward_down_component=ward_down_no[ward_down_no$source=="KEGG",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)

down_cluster_all_process=down_output_no#tabella da vedere


ward_down_component=ward_down_no[ward_down_no$source=="GO:CC",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)
down_vitro_component=down_output_no

setwd(base_path)
ward_down_no=read.csv("up_ana_last_version.csv")
#go_up_34_function=go_up_34[go_up_34$source=="GO:MF",]
#up_34_function=as.vector(go_up_34_function$term_name)
#go_up_34_process=go_up_34[go_up_34$source=="GO:BP",]
#up_34_process=as.vector(go_up_34_process$term_name)

ward_down_component=ward_down_no[ward_down_no$source=="TF",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)

down_cluster_all_function=down_output_no#tabella da vedere

ward_down_component=ward_down_no[ward_down_no$source=="GO:BP",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)

down_cluster_all_process=down_output_no#tabella da vedere


ward_down_component=ward_down_no[ward_down_no$source=="GO:CC",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)
up_vitro_component=down_output_no

setwd(base_path)
ward_down_no=read.csv("down_vivo_last_version.csv")
#go_up_34_function=go_up_34[go_up_34$source=="GO:MF",]
#up_34_function=as.vector(go_up_34_function$term_name)
#go_up_34_process=go_up_34[go_up_34$source=="GO:BP",]
#up_34_process=as.vector(go_up_34_process$term_name)

ward_down_component=ward_down_no[ward_down_no$source=="GO:MF",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)

down_cluster_all_function=down_output_no#tabella da vedere

ward_down_component=ward_down_no[ward_down_no$source=="GO:BP",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)

down_cluster_all_process=down_output_no#tabella da vedere


ward_down_component=ward_down_no[ward_down_no$source=="GO:CC",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)
down_vivo_component=down_output_no

setwd(base_path)
ward_down_no=read.csv("up_vivo_last_version.csv")
#go_up_34_function=go_up_34[go_up_34$source=="GO:MF",]
#up_34_function=as.vector(go_up_34_function$term_name)
#go_up_34_process=go_up_34[go_up_34$source=="GO:BP",]
#up_34_process=as.vector(go_up_34_process$term_name)

ward_down_component=ward_down_no[ward_down_no$source=="GO:MF",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)

down_cluster_all_function=down_output_no#tabella da vedere

ward_down_component=ward_down_no[ward_down_no$source=="GO:BP",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)

down_cluster_all_process=down_output_no#tabella da vedere


ward_down_component=ward_down_no[ward_down_no$source=="GO:CC",]
down_component_no=as.vector(ward_down_component$term_name)
down_pvalue_no=as.vector(ward_down_component$adjusted_p_value)
down_output_no=data.frame(down_component_no,down_pvalue_no)
up_vivo_component=down_output_no

Extended Figure 10 C

library(UpSetR)
down_vivo_component=down_vivo_component$down_component_no
up_vivo_component=up_vivo_component$down_component_no
down_vitro_component=down_vitro_component$down_component_no
up_vitro_component=up_vitro_component$down_component_no

listInput <- list(up_vitro_component = up_vitro_component, down_vitro_component =down_vitro_component, up_vivo_component = up_vivo_component,down_vivo_component=down_vivo_component)

upset(fromList(listInput), order.by = "freq")

Comaprison with data from Cheng, S. et al. Single-Cell RNA-Seq Reveals Cellular Heterogeneity of Pluripotency Transition and X Chromosome Dynamics during Early Mouse Development. Cell Rep 26, 2593-2607 e2593 (2019).

setwd("/Users/gabriele.lubatti/Desktop/Phd/Cell_Competition/Cell_Competition_Github/SC_RNA_Seq_published_dataset/Cheng_et_al_2019")
#bo=read.csv("GSE109071_series_matrix.txt",sep="\t",header = F)


meta_dati_1=read.csv("SraRunTable.txt.csv")
meta_dati=read.table("sample.tsv",sep="\t",header=T)
dati_raw=read.table("GSE109071_read.txt")
age=meta_dati_1$Age
age=(as.vector(age))
age_new=age[age=="embryonic day 6.5"|age=="embryonic day 5.5" |age=="embryonic day 6.25"]

index=age=="embryonic day 6.5"|age=="embryonic day 5.5" |age=="embryonic day 6.25"

sample=meta_dati_1$Sample.Name
sample=as.vector(sample)


accession=meta_dati$Accession

nome=as.vector(meta_dati$Title)


nome=substring(nome,19,nchar(nome))

provo_1=data.frame(accession,nome)
row.names(provo_1)=accession
provo_2=provo_1[sample,]
provo_3=provo_2


dati_raw_1=t(dati_raw)
dati_raw_2=dati_raw_1[as.vector(provo_3$nome)[as.vector(provo_3$nome)%in%colnames(dati_raw)],]
dati_raw_3=t(dati_raw_2)


nome_3=nome_gene$Gene.name[nome_gene$Gene.name%in%row.names(dati_raw_3)]
index_3=nome_gene$Gene.stable.ID[nome_gene$Gene.name%in%row.names(dati_raw_3)]
dati_raw_4=dati_raw_3[nome_3,]
row.names(dati_raw_4)=index_3

nome_index=as.vector(provo_3$nome[index])

dati_raw_5=dati_raw_4[,nome_index]

data_raw_fmk=data_raw[ ,cells_epi_fmk]
data_tpm_fmk=data_tpm[,cells_epi_fmk]

data_raw_dmso=data_raw[,cells_epi_dmso]
data_tpm_dmso=data_tpm[,cells_epi_dmso]

geni_comuni_nuovi=intersect(row.names(dati_raw_5),row.names(data_raw_fmk))
dati_raw_5=dati_raw_5[geni_comuni_nuovi,]
data_raw_fmk=data_raw_fmk[geni_comuni_nuovi,]
data_tpm_fmk=data_tpm_fmk[geni_comuni_nuovi,]
data_raw_dmso=data_raw_dmso[geni_comuni_nuovi,]
data_tpm_dmso=data_tpm_dmso[geni_comuni_nuovi,]

data_pro <- CreateSeuratObject(counts = dati_raw_5, project = "data_wolf")
#data_diego$stim <- "MUC13230"
#data_1_seurat$isnt <- "IPS"
#print(head(x = rownames(x = SIGAH12)))
#print(head(x = colnames(x = SIGAH12)))
#print(SIGAH12)
data_pro<- subset(data_pro, subset = nFeature_RNA > 0)
data_pro <- NormalizeData(data_pro, verbose = FALSE)

data_pro<- FindVariableFeatures(data_pro, selection.method = "vst", nfeatures = 3000)
data_pro<- ScaleData(data_pro, verbose = FALSE)

data_pro <- RunPCA(data_pro, npcs = 30, verbose = FALSE)
data_pro<- RunUMAP(data_pro, reduction = "pca", dims = 1:20,set.seed=42)

## Warning: The following arguments are not used: set.seed

## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session

## 16:27:11 UMAP embedding parameters a = 0.9922 b = 1.112

## 16:27:11 Read 1310 rows and found 20 numeric columns

## 16:27:11 Using Annoy for neighbor search, n_neighbors = 30

## 16:27:11 Building Annoy index with metric = cosine, n_trees = 50

## 0%   10   20   30   40   50   60   70   80   90   100%

## [----|----|----|----|----|----|----|----|----|----|

## **************************************************|
## 16:27:11 Writing NN index file to temp file /var/folders/sh/dxnz33055m57vnkk6rd8d_b561qpqw/T//RtmpATlMgp/file139875eb7d796
## 16:27:11 Searching Annoy index using 1 thread, search_k = 3000
## 16:27:12 Annoy recall = 100%
## 16:27:12 Commencing smooth kNN distance calibration using 1 thread
## 16:27:14 Initializing from normalized Laplacian + noise
## 16:27:14 Commencing optimization for 500 epochs, with 47972 positive edges
## 16:27:16 Optimization finished

data_pro <- RunTSNE(data_pro, reduction = "pca", dims = 1:20)
data_pro <- FindNeighbors(data_pro, reduction = "pca", dims = 1:20,k.param=20)

## Computing nearest neighbor graph
## Computing SNN

data_pro <- FindClusters(data_pro, resolution =0.003)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 1310
## Number of edges: 34879
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9985
## Number of communities: 3
## Elapsed time: 0 seconds

data_pro$"label"=age_new
library(repr)
library(cowplot)
options(repr.plot.width=8, repr.plot.height=3)
#plot batches and clusters
#p1 <- DimPlot(ips_combined, reduction = "umap", group.by = "stim", dim.1 = 10, dim.2 = 10)
p2 <- DimPlot(data_pro, reduction = "umap", label = TRUE, dims = c(1,2))
p3 <- DimPlot(data_pro, reduction = "pca", label = TRUE, dims = c(1,2))
plt <- plot_grid( p2,p3,rel_widths = c(1.1, 1))
plt

DefaultAssay(data_pro) <- "RNA"



p3 <- DimPlot(data_pro, reduction = "pca", label = TRUE, dims = c(1,2),group.by = "label")
plt <- plot_grid(p3,rel_widths = c(1.1, 1))
plt

p3 <- DimPlot(data_pro, reduction = "umap", label = TRUE, dims = c(1,2),group.by = "label")

p3 <- DimPlot(data_pro, reduction = "umap", label = TRUE, dims = c(1,2),group.by = "label")

plt <- plot_grid(p3,rel_widths = c(1.1, 1))
plt

p3 <- DimPlot(data_pro, reduction = "tsne", label = TRUE, dims = c(1,2))

plt <- plot_grid(p3,rel_widths = c(1.1, 1))
plt

hvg_pro=data_pro@assays$RNA@var.features


DefaultAssay(data_pro) <- "RNA"
PlotGeneExpression <- function(Sobj,gname){
    #plot gene expression in UMAP
    p1 <- FeaturePlot(Sobj, reduction="pca",features = c(gname))
    #plot UMAP colored by cluster
    #p2 <- DimPlot(Sobj, reduction = "umap", label = TRUE)
    #plt <- plot_grid(p1, p2,rel_widths = c(1.2, 1))
    #plot UMAP colored according to IVF/NT condition
    p3 <- DimPlot(Sobj, reduction = "pca", group.by = "label")
    
    #p4 <- VlnPlot(Sobj, features = c(gname))
    
    #Arrange them together in a grid
    #top_row <- plot_grid(p1, p2, p3)
    plot_grid(p1, ncol = 1, nrow=2)
}

# EXAMPLE OF USAGE
#nome_gene$Gene.stable.ID[nome_gene$Gene.name=="Pou5f1"]
gname<-nome_gene$Gene.stable.ID[nome_gene$Gene.name=="Pou5f1"]
PlotGeneExpression(data_pro,gname)

gname<-nome_gene$Gene.stable.ID[nome_gene$Gene.name=="Bmp4"]
PlotGeneExpression(data_pro,gname)

gname<-nome_gene$Gene.stable.ID[nome_gene$Gene.name=="Amn"]
PlotGeneExpression(data_pro,gname)

#Cellule ExE
dati_pca=(Embeddings(data_pro, reduction = "pca")[, 1:2])
dati_pca=as.data.frame(dati_pca)

cellule_exe_1=row.names(dati_pca)[(dati_pca$PC_1 >-5) & (dati_pca$PC_2<-20)]

projec<-as.matrix(GetAssayData(data_pro, slot ="data",assay="RNA"))
logico=projec[nome_gene$Gene.stable.ID[nome_gene$Gene.name=="Bmp4"],]>1.5
cellule_exe_2=colnames(projec)[logico]


cellule_exe=intersect(cellule_exe_1,cellule_exe_2)


#Cellule Epiblasto
dati_pca=(Embeddings(data_pro, reduction = "pca")[, 1:2])
dati_pca=as.data.frame(dati_pca)

dim(dati_pca)

## [1] 1310    2

cellule_epi_2=row.names(dati_pca)[(dati_pca$PC_1>5)&(dati_pca$PC_2<20)]

projec<-as.matrix(GetAssayData(data_pro, slot ="data",assay="RNA"))
logico=projec[nome_gene$Gene.stable.ID[nome_gene$Gene.name=="Pou5f1"],]>1.5
cellule_epi_1=colnames(projec)[logico]


cellule_epi=intersect(cellule_epi_1,cellule_epi_2)

#Cellule VE

dati_pca=(Embeddings(data_pro, reduction = "pca")[, 1:2])
dati_pca=as.data.frame(dati_pca)

dim(dati_pca)

## [1] 1310    2

cellule_ve_2=row.names(dati_pca)[(dati_pca$PC_1<0)&(dati_pca$PC_2<0)]

projec<-as.matrix(GetAssayData(data_pro, slot ="data",assay="RNA"))
logico=projec[nome_gene$Gene.stable.ID[nome_gene$Gene.name=="Amn"],]>1.5
cellule_ve_1=colnames(projec)[logico]




cellule_ve=intersect(cellule_ve_1,cellule_ve_2)

cellule_all=c(cellule_epi,cellule_exe,cellule_ve)

cellule_all=cellule_all[isUnique(cellule_all)]



logico_2=as.vector(provo_3$nome)%in%cellule_all
age_spero=age[logico_2]
cell_all_new=as.vector(provo_3$nome)[as.vector(provo_3$nome)%in%cellule_all]
#lavoro con cellule_epi_new ,age_spero e projec_new


projec_new=projec[,cell_all_new]


dati_raw_6=dati_raw_5[,cell_all_new]



cell_type=rep(0,length(cell_all_new))
cell_type[cell_all_new%in%cellule_epi]="Epiblast"
cell_type[cell_all_new%in%cellule_exe]="ExE"
cell_type[cell_all_new%in%cellule_ve]="VE"

Epiblast cells at stages E5.5, E6.25 and E6.5 (Cheng et al)

dati_pca=(Embeddings(data_pro, reduction = "pca")[, 1:2])
dati_pca=as.data.frame(dati_pca)

dim(dati_pca)

## [1] 1310    2

cellule_epi_2=row.names(dati_pca)[dati_pca$PC_1>5&dati_pca$PC_2<20]

projec<-as.matrix(GetAssayData(data_pro, slot ="data",assay="RNA"))
logico=projec[nome_gene$Gene.stable.ID[nome_gene$Gene.name=="Pou5f1"],]>1.5
cellule_epi_1=colnames(projec)[logico]


cellule_epi=intersect(cellule_epi_1,cellule_epi_2)

logico_2=as.vector(provo_3$nome)%in%cellule_epi
age_spero=age[logico_2]
cell_epi_new=as.vector(provo_3$nome)[as.vector(provo_3$nome)%in%cellule_epi]
#lavoro con cellule_epi_new ,age_spero e projec_new


projec_new=projec[,cell_epi_new]


dati_raw_6=dati_raw_5[,cell_epi_new]

data_de=dati_raw_6
#tengo solo i geni con media tpm maggiore di 10 nei miei vecchi dati
geni=row.names(data_de)[row.names(data_de)%in%row.names(data_tpm)]
dati_confronto=data_de[geni,]
setwd(base_path)
#lunghezze=getGeneLengthAndGCContent(row.names(dati_confronto), org="mmu")
#lunghezze_data=as.data.frame(lunghezze)
#save(lunghezze_data,file="lunghezze_data.Rda")
load("length_genes.Rda")
#lunghezze_data_wolf=lunghezze_data[geni,]

geni_nuovo_nuovo=row.names(lunghezze_data)[!(is.na(lunghezze_data$length))]
lunghezze_vere=lunghezze_data$length[!(is.na(lunghezze_data$length))]

geni_tengo=geni_nuovo_nuovo[geni_nuovo_nuovo%in%row.names(dati_confronto)]
lunghezze_vere_vere=lunghezze_vere[geni_nuovo_nuovo%in%row.names(dati_confronto)]


dati_confronto_nuovi=dati_confronto[geni_tengo,]


sce_jon<-SingleCellExperiment(list(counts=as.matrix(dati_confronto_nuovi)))
tpm_proiezione_jon=calculateTPM(sce_jon,lengths=lunghezze_vere_vere)


sce_pro_jon<- SingleCellExperiment(assays = list(normcounts = as.matrix(tpm_proiezione_jon)))
logcounts(sce_pro_jon) <- log10(normcounts(sce_pro_jon) + 1)
rowData(sce_pro_jon)$feature_symbol <- rownames(sce_pro_jon)

data_6_5 <- CreateSeuratObject(counts = data_de, project = "data_wolf")
#data_diego$stim <- "MUC13230"
#data_1_seurat$isnt <- "IPS"
#print(head(x = rownames(x = SIGAH12)))
#print(head(x = colnames(x = SIGAH12)))
#print(SIGAH12)
data_6_5<- subset(data_6_5, subset = nFeature_RNA > 0)
data_6_5 <- NormalizeData(data_6_5, verbose = FALSE)

data_6_5<- FindVariableFeatures(data_6_5, selection.method = "vst", nfeatures = 1000)
HVGC=VariableFeatures(data_6_5)

dist_computation=function(a,b){
  log_data_tpm <- log10(a+1)[b,]
#save(log_data_tpm,file="chosen_exprs1_novembre.Rda")
#load(file="chosen_exprs1_novembre.Rda")
log_data_tpm<-as.matrix(log_data_tpm)
dissimilarity <- sqrt((1-cor((log_data_tpm), method = "spearman"))/2)
#save(dissimilarity,file="dissimilarity_all.Rda")
#Umap by medium

return(dissimilarity)
}

geni_comuni=HVGC[HVGC%in%row.names(tpm_proiezione_jon)]
tpm_proiezione_jon=tpm_proiezione_jon[geni_comuni,]

data_tpm_fmk_jon=data_tpm_fmk[geni_comuni,]
all_data=cbind(data_tpm_fmk_jon,tpm_proiezione_jon)
#all_data=data_tpm_fmk
#load("HVGC_giusto_no_un.Rda")
#load("dissimilarity_all.Rda")
#dissimilarity_all=dissimilarity
#log_data_tpm <- log10(data_tpm[HVGC,]+1)


#log_data_tpm<-as.matrix(log_data_tpm)
#dissimilarity_all <- sqrt((1-cor((log_data_tpm), method = "spearman"))/2)

dissimilarity_all=dist_computation(all_data,geni_comuni)
#dissimilarity_fmk=dist_computation(fmk,hvg_wolf)

Extended Figure 3 E and F

set.seed(1000)
dissimilarity_fmk=dissimilarity_all[colnames(data_tpm_fmk_jon),colnames(tpm_proiezione_jon)]
dissimilarity_jon=dissimilarity_all[colnames(tpm_proiezione_jon),colnames(tpm_proiezione_jon)]

dm=DiffusionMap(dissimilarity_jon,distance='cosine')

## Warning in DiffusionMap(dissimilarity_jon, distance = "cosine"): You have 520
## genes. Consider passing e.g. n_pcs = 50 to speed up computation.

#fmk=fmk[hvg_fmk,]
#dm=DiffusionMap(t(fmk))
DC1=dm$DC1
DC2=dm$DC2
#Cluster=as.factor(Cluster_col)
#clusters_fmk=as.vector(wolf_6_5$label)
#clusters=meta_dati_course[cells_epi_fmk,]$cluster
clusters=age_spero
clusters[age_spero=="embryonic day 5.5"]="5"
clusters[age_spero=="embryonic day 6.25" ]="6"
clusters[age_spero=="embryonic day 6.5" ]="7"
#clusters[clusters==1]="Normal (winner) Epiblast"
#clusters[clusters==3]="Intermediate"
#clusters[clusters==4]="Loser Epiblast"
Cluster=as.factor(clusters)
tre=data.frame(DC1,DC2,Cluster)
#dpt <- DPT(dm,tips=20)
#dpt <- DPT(dm)


#pdf("figure_paper_pdf/diffusion_map_all.pdf",width=8,height=5)
 diffu<-ggplot(tre, aes(x=DC1 , y=DC2 , color= Cluster)) + geom_point()+
labs(x = "DC1",y="DC2",col="Clusters") + ggtitle("Diffusion Map")+
   scale_color_manual(breaks = c("5","6","7")
                                   ,labels = c("Epiblast 5.5"
                                              
                                              
                                              
                                              ,"Epiblast 6.25"
                                              ,"Epiblast 6.5"
                                              
                                              
                                              
                                              )
                                              
                                             
                                   ,values=c("grey","yellow","red" ))+
   labs(col="Cluster")
 
 
diffu

#dpt <- DPT(dm)


dpt <- DPT(dm,tips=150)
#save(dpt,file="dpt.Rda")
#str(dpt)
tips(dpt)

## EB_2223  EB_950 EB_2054 
##     150     333     102

plot(dpt, col_by = 'DPT333')

#wolf<-as.matrix(GetAssayData(wolf_6_5, slot ="data",assay="RNA"))
#wolf=wolf[hvg_fmk,]
pred=dm_predict(dm,(dissimilarity_fmk))





prima=c(dm$DC1,pred[,1]) 
seconda=c(dm$DC2,pred[,2])
clusters_pro_fmk=meta_dati_course[cells_epi_fmk,]$cluster
#clusters_pro=meta_dati_course[cells_epi_dmso,]$cluster
#clusters_pro[clusters_pro==1]="5"
#clusters_pro[clusters_pro==3]="6"
#clusters_pro=rep(5,length(colnames(data_tpm_dmso)))
#Cluster_pro=as.factor(clusters_pro)

colore_plot=c(clusters,clusters_pro_fmk)
colore_plot=as.factor(colore_plot)

dati_plot=data.frame(prima,seconda,colore_plot)
diffu<-ggplot(dati_plot, aes(x=prima , y=seconda , color=(colore_plot))) + geom_point()+labs(x = "DC1",y="DC2") + ggtitle("Diffusion Map")+labs(col="Cluster")+scale_color_manual(breaks = c("1","3","4","5","6","7")
                                   ,labels = c("Normal (winner) Epiblast"
                                              
        
                                              ,"Intermediate"
                                              ,"Loser Epiblast "
                                              ,"Epiblast 5.5"
                                              ,"Epiblast 6.25"
                                              ,"Epiblast 6.5"
                                              
                                              
                                              )
                                              
                                             
                                   ,values=c("#DD6400","#0000FF","#006400","grey","yellow","red" ))


diffu

time_dati_nuovi=dpt$DPT333
projection_dist_ten=function (dm, new_dcs = NULL, ..., new_data, verbose = FALSE) 
{
    if (is.null(new_dcs)) 
        new_dcs <- dm_predict(dm, new_data, ..., verbose = verbose)
    else if (!missing(new_data)) 
        stop("only pass one of new_dcs and new_data")
    evs <- eigenvectors(dm)
    nns <- find_knn(evs, 10, query = as.matrix(new_dcs))
    nn_dist <- nns$index[, 1:10]
    #nn_dist
}







dist=projection_dist_ten(dm,new_dcs=pred)
k10=matrix(0,ncol=10,nrow = length(colnames(data_tpm_fmk_jon)))
for( i in 1:length(colnames(data_tpm_fmk_jon))){
k10[i,]=time_dati_nuovi[dist[i,]]
#print(i)
}
time_projec_fmk=apply(k10,1,mean)

dfForPlot <- data.frame(clusters_fmk = as.character(c(clusters,clusters_pro_fmk))
                                ,exp=c(time_dati_nuovi,time_projec_fmk)
                                ,Cluster = as.factor(as.character(c(clusters,clusters_pro_fmk))))



ggplot(data =dfForPlot
                    ,aes(x = clusters_fmk
                         ,y =  exp
                         ,col = Cluster )) +
                geom_boxplot(width=0.9,alpha=0.8) + 
  
  
                scale_color_manual(breaks = c("1","3","4","5","6","7")
                                   ,labels = c("Normal (winner) Epiblast"
                                              
                                              
                                              
                                              ,"Intermediate"
                                              ,"Loser Epiblast "
                                              ,"Epiblast 5.5"
                                              ,"Epiblast 6.25"
                                              ,"Epiblast 6.5"
                                              
                                              )
                                              
                                             
                                   ,values=c("#DD6400","#0000FF","#006400" ,"grey","yellow","red")) +
                
  ylab("DPT")+xlab(NULL) + 
  geom_sina()+
  ggtitle("Pseudo time FMK cells (274) and epiblast cells E5.5, E6.25 and E6.5 (520)")+ylim(c(0,max(time_dati_nuovi)))+theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

#pdf("losing_score_dmso_projection.pdf",useDingbats = F)

Projection DMSO epiblast cells

geni_comuni=HVGC[HVGC%in%row.names(tpm_proiezione_jon)]
tpm_proiezione_jon=tpm_proiezione_jon[geni_comuni,]

data_tpm_dmso_jon=data_tpm_dmso[geni_comuni,]
all_data=cbind(data_tpm_dmso_jon,tpm_proiezione_jon)
#all_data=data_tpm_fmk
#load("HVGC_giusto_no_un.Rda")
#load("dissimilarity_all.Rda")
#dissimilarity_all=dissimilarity
#log_data_tpm <- log10(data_tpm[HVGC,]+1)


#log_data_tpm<-as.matrix(log_data_tpm)
#dissimilarity_all <- sqrt((1-cor((log_data_tpm), method = "spearman"))/2)

dissimilarity_all=dist_computation(all_data,geni_comuni)
#dissimilarity_fmk=dist_computation(fmk,hvg_wolf)

set.seed(1001)
dissimilarity_dmso=dissimilarity_all[colnames(data_tpm_dmso_jon),colnames(tpm_proiezione_jon)]
dissimilarity_jon=dissimilarity_all[colnames(tpm_proiezione_jon),colnames(tpm_proiezione_jon)]

dm=DiffusionMap(dissimilarity_jon,distance="cosine")

## Warning in DiffusionMap(dissimilarity_jon, distance = "cosine"): You have 520
## genes. Consider passing e.g. n_pcs = 50 to speed up computation.

#dm=DiffusionMap(dissimilarity_jon)
#fmk=fmk[hvg_fmk,]
#dm=DiffusionMap(t(fmk))
DC1=dm$DC1
DC2=dm$DC2
#Cluster=as.factor(Cluster_col)
#clusters_fmk=as.vector(wolf_6_5$label)
#clusters=meta_dati_course[cells_epi_fmk,]$cluster
clusters=age_spero
clusters[age_spero=="embryonic day 5.5"]="5"
clusters[age_spero=="embryonic day 6.25" ]="6"
clusters[age_spero=="embryonic day 6.5" ]="7"
#clusters[clusters==1]="Normal (winner) Epiblast"
#clusters[clusters==3]="Intermediate"
#clusters[clusters==4]="Loser Epiblast"
Cluster=as.factor(clusters)
tre=data.frame(DC1,DC2,Cluster)
#dpt <- DPT(dm,tips=20)
#dpt <- DPT(dm)


#pdf("figure_paper_pdf/diffusion_map_all.pdf",width=8,height=5)
 diffu<-ggplot(tre, aes(x=DC1 , y=DC2 , color= Cluster)) + geom_point()+
labs(x = "DC1",y="DC2",col="Clusters") + ggtitle("Diffusion Map")+
   scale_color_manual(breaks = c("5","6","7")
                                   ,labels = c("Epiblast 5.5"
                                              
                                              
                                              
                                              ,"Epiblast 6.25"
                                              ,"Epiblast 6.5"
                                              
                                              
                                              
                                              )
                                              
                                             
                                   ,values=c("grey","yellow","red" ))+
   labs(col="Cluster")
 
diffu

#dpt <- DPT(dm)


dpt <- DPT(dm,tips=150)
#save(dpt,file="dpt.Rda")
#str(dpt)
tips(dpt)

## EB_2223  EB_950 EB_2054 
##     150     333     102

plot(dpt, col_by = 'DPT333')

#wolf<-as.matrix(GetAssayData(wolf_6_5, slot ="data",assay="RNA"))
#wolf=wolf[hvg_fmk,]
pred=dm_predict(dm,(dissimilarity_dmso))





prima=c(dm$DC1,pred[,1]) 
seconda=c(dm$DC2,pred[,2])
clusters_pro_dmso=meta_dati_course[cells_epi_dmso,]$cluster
#clusters_pro=meta_dati_course[cells_epi_dmso,]$cluster
#clusters_pro[clusters_pro==1]="5"
#clusters_pro[clusters_pro==3]="6"
#clusters_pro=rep(5,length(colnames(data_tpm_dmso)))
#Cluster_pro=as.factor(clusters_pro)

colore_plot=c(clusters,clusters_pro_dmso)
colore_plot=as.factor(colore_plot)

dati_plot=data.frame(prima,seconda,colore_plot)
diffu<-ggplot(dati_plot, aes(x=prima , y=seconda , color=(colore_plot))) + geom_point()+labs(x = "DC1",y="DC2") + ggtitle("Diffusion Map")+labs(col="Cluster")+scale_color_manual(breaks = c("1","3","5","6","7")
                                   ,labels = c("Normal (winner) Epiblast"
                                              
        
                                              ,"Intermediate"
                                             
                                              ,"Epiblast 5.5"
                                              ,"Epiblast 6.25"
                                              ,"Epiblast 6.5"
                                              
                                              
                                              )
                                              
                                             
                                   ,values=c("#DD6400","#0000FF","grey","yellow","red" ))

  diffu

time_dati_nuovi=dpt$DPT333
projection_dist_ten=function (dm, new_dcs = NULL, ..., new_data, verbose = FALSE) 
{
    if (is.null(new_dcs)) 
        new_dcs <- dm_predict(dm, new_data, ..., verbose = verbose)
    else if (!missing(new_data)) 
        stop("only pass one of new_dcs and new_data")
    evs <- eigenvectors(dm)
    nns <- find_knn(evs, 10, query = as.matrix(new_dcs))
    nn_dist <- nns$index[, 1:10]
    #nn_dist
}







dist=projection_dist_ten(dm,new_dcs=pred)
k10=matrix(0,ncol=10,nrow = length(colnames(data_tpm_dmso_jon)))
for( i in 1:length(colnames(data_tpm_dmso_jon))){
k10[i,]=time_dati_nuovi[dist[i,]]
#print(i)
}
time_projec_dmso=apply(k10,1,mean)

Extended Figure 3 G

dfForPlot <- data.frame(clusters_tutto = as.character(c(clusters,clusters_pro_fmk,clusters_pro_dmso))
                                ,exp=c(time_dati_nuovi,time_projec_fmk,time_projec_dmso)
                                ,Cluster = as.factor(as.character(c(clusters,clusters_pro_fmk,clusters_pro_dmso))))

clusters_dmso=clusters_pro_dmso
clusters_fmk=clusters_pro_fmk
clusters_ori=clusters

clusters_dmso[clusters_pro_dmso%in%3]="5"
clusters_dmso[clusters_pro_dmso%in%1]="4"

clusters_fmk[clusters_pro_fmk%in%4]="3"
clusters_fmk[clusters_pro_fmk%in%3]="2"
clusters_fmk[clusters_pro_fmk%in%1]="1"

clusters_ori[clusters%in%5]="6"
clusters_ori[clusters%in%6]="7"
clusters_ori[clusters%in%7]="8"

clusters_all=c(clusters_ori,clusters_fmk,clusters_dmso)

nuovo_ordine <- factor(clusters_all,
    levels = c("1","2","3","4","5","6","7","8",ordered = TRUE))
#all_clu=c(ci_name,dmso_name)





ggplot(data =dfForPlot
                    ,aes(x = nuovo_ordine
                         ,y =  dfForPlot$exp
                         ,col = nuovo_ordine )) +
                geom_boxplot(width=0.9,alpha=0.8) + 
  
  
                scale_color_manual(breaks = c("1","2","3","4","5","6","7","8")
                                   ,labels = c("Winner Epiblast-CI"
                                               ,"Intermediate-CI"
                                              ,"Loser Epiblast-CI"
                                              ,"Winner Epiblast-DMSO"
                                              ,"Intermediate-DMSO"
                                              
                                              ,"Epiblast 5.5"
                                              ,"Epiblast 6.25"
                                              ,"Epiblast 6.5"
                                              
                                              )
                                              
                                             
                                   ,values=c("#DD6400","#0000FF","#006400","#DD6400","#0000FF", "grey","yellow","red")) +
                
  ylab("DPT")+xlab(NULL) + labs(col="Cluster")+
  geom_sina()+
  ggtitle("Pseudo time CI (274), DMSO (238) and epiblast cells E5.5, E6.25 and E6.5 (520)")+ylim(c(0,max(time_dati_nuovi)))+
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank())

## Warning: Use of `dfForPlot$exp` is discouraged. Use `exp` instead.

## Warning: Use of `dfForPlot$exp` is discouraged. Use `exp` instead.

#pdf("losing_score_dmso_projection.pdf",useDingbats = F)

Extended Figure 2 A

data_raw_fmk=data_raw[,cells_epi_fmk]
data_raw_dmso=data_raw[,cells_epi_dmso]

meta_dati_course_fmk=meta_dati_course[cells_epi_fmk,]
meta_dati_course_dmso=meta_dati_course[cells_epi_dmso,]

mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", package="scran"))
out1<-list(g1=mm.pairs$G1,g2=mm.pairs$G2M,s=mm.pairs$S)

cyc<-cyclone(as.matrix(data_raw_fmk),out1)
score_r<-cyc$scores
g1_max<-score_r$g1>= 0.5 & score_r$g1>=score_r$g2

g2_max<-score_r$g2>=0.5 & score_r$g2>score_r$g1

s_max<-score_r$g1<0.5 & score_r$g2<0.5

sequenza_fmk<-rep(0,length(cells_epi_fmk))
sequenza_fmk[g1_max]="G1"
sequenza_fmk[s_max]="S"
sequenza_fmk[g2_max]="G2M"

provo_fmk=as.factor(sequenza_fmk)
provo_fmk <- factor(provo_fmk, levels = c("G2M", "S", "G1"))


cyc<-cyclone(as.matrix(data_raw_dmso),out1)
score_r<-cyc$scores
g1_max<-score_r$g1>= 0.5 & score_r$g1>=score_r$g2

g2_max<-score_r$g2>=0.5 & score_r$g2>score_r$g1

s_max<-score_r$g1<0.5 & score_r$g2<0.5

sequenza_dmso<-rep(0,length(cells_epi_dmso))
sequenza_dmso[g1_max]="G1"
sequenza_dmso[s_max]="S"
sequenza_dmso[g2_max]="G2M"



provo_dmso=as.factor(sequenza_dmso)
provo_dmso <- factor(provo_dmso, levels = c("G2M", "S", "G1"))

provo_all=c(sequenza_fmk,sequenza_dmso)
provo_all <- factor(provo_all, levels = c("G2M", "S", "G1"))





ci_name=meta_dati_course_fmk$cluster
ci_name[ci_name==3]="Intermediate-CI"
ci_name[ci_name==4]="Los Epi-CI"
ci_name[ci_name==1]="Win Epi-CI"

dmso_name=meta_dati_course_dmso$cluster
dmso_name[dmso_name==3]="Intermediate-DMSO"
dmso_name[dmso_name==4]="Los Epi-DMSO"
dmso_name[dmso_name==1]="Win Epi-DMSO"
name_full=c(ci_name,dmso_name)

nuovo_ordine <- factor(name_full,
    levels = c("Win Epi-CI","Win Epi-DMSO","Intermediate-CI","Intermediate-DMSO","Los Epi-CI","Los Epi-DMSO",ordered = TRUE))
#all_clu=c(ci_name,dmso_name)


dfForPlot <- data.frame(Phases =provo_all
                                ,clu =nuovo_ordine)
pnm <-ggplot(dfForPlot, aes(y=Phases,x=clu))
pnm +geom_bar( aes(fill = Phases,y = (..count..)/sum(..count..)),position="fill")+ ggtitle(paste( as.character("Cell cycle CI and DMSO cells")))+ylab("Fraction of cells")+xlab("Cluster")

dfForPlot <- data.frame(Phases =sequenza_fmk
                                ,clu =as.factor(meta_dati_course_fmk$cluster))
dfForPlot <- data.frame(Phases =provo_all
                                ,clu =nuovo_ordine)
pnm <-ggplot(dfForPlot, aes(y=Phases,x=clu))
pnm +geom_bar( aes(fill = Phases,y = (..count..)))+ ggtitle(paste( as.character("Cell cycle CI and DMSO cells")))+ylab("Number of cells")+xlab("Cluster")

Heteroplasmy

setwd(base_path)
load("meta_dati.Rda")
nomi=read.csv("old_2_cell_names.csv")
nomi=nomi$X0
nomi=as.vector(nomi)
setwd(base_path)
all_pos=read.csv("raw_counts_allele_mt.csv")
all_pos=all_pos[,-1]
colnames(all_pos)=substring(colnames(all_pos),2,nchar(colnames(all_pos)))


non_so=gsub("[.]", "_", colnames(all_pos))
colnames(all_pos)=non_so
row.names(all_pos)=substring(nomi,12,nchar(nomi)-25)
vedere=substring(colnames(all_pos),1,nchar(colnames(all_pos))-3)
vedere_numb=substring(vedere,1,nchar(vedere)-1)

#nomi cellule e cluster
setwd(base_path)
load("provo_ward_no_un.Rda")
load("name_full.Rda")#name of the old bam file
#load("nomi_nuovi.Rda")#name on array express
#load("nomi_vecchi.Rda")#
nomi_nuovi=meta_dati$nomi_nuovi
nomi_vecchi=meta_dati$nomi_vecchi
row.names(provo_ward_no_un)=provo_ward_no_un$Cellname
provo_ward_no_un=provo_ward_no_un[nomi_full,]

load("provo.Rda")
cells_epi=row.names(provo)


row.names(provo_ward_no_un)=cells_epi
provo_1=provo_ward_no_un[!(grepl("2",provo_ward_no_un$cluster)),]
provo_1$nomi_vecchi=row.names(provo_1)
old=nomi_vecchi[nomi_vecchi%in%row.names(provo_1)]
provo_2=provo_1[old,]
new=nomi_nuovi[nomi_vecchi%in%row.names(provo_1)]
provo_2$nomi_nuovi=new
row.names(provo_2)=provo_2$Cellname
cell_common=row.names(all_pos)[row.names(all_pos)%in%row.names(provo_2)]
provo_3=provo_2[cell_common,]

provo_4=provo_3[grep("FMK",provo_3$condizione),]
cells_fmk=row.names(provo_4)

provo_5=provo_3[grep("DMSO",provo_3$condizione),]
cells_dmso=row.names(provo_5)

cell_clu_1=row.names(provo_4)[provo_4$cluster==1]
cell_clu_3=row.names(provo_4)[provo_4$cluster==3]
cell_clu_4=row.names(provo_4)[provo_4$cluster==4]

all_posizioni=all_pos
vedere_all=non_so
vedere=vedere_numb

entropy_method=function(all_pos,cell_stage,vedere_all,vedere_numb,number_reads,number_positions,filtering=1,selection=FALSE,cell_clu_1=NULL,cell_clu_3=NULL,cell_clu_4=NULL){
all_posizioni=all_pos[cell_stage,]
vedere_all=non_so
vedere=vedere_numb
all_pos=all_pos[cell_stage,]
prova=matrix(0,ncol=length(unique(vedere)),nrow = length(row.names(all_posizioni)))
colnames(prova)=unique(vedere)
row.names(prova)=row.names(all_posizioni)
colnames(all_posizioni)=vedere

for (i in unique(vedere)){
  
prova[,i]=apply(all_posizioni[,colnames(all_posizioni)%in%i],1,function(x){
  
  
  x=as.numeric(as.vector(x))
  
  return(sum(x))
  })
}

somma=prova
#save(somma,file="somma_antonio.Rda")
#load(file="somma_antonio.Rda")

#First filtering step
somma=as.matrix(somma)
pos_cov=apply(somma,1,function(x){sum(x>number_reads)})

dfForPlot <- data.frame(clusters =row.names(somma)
                                ,genes_expression = as.numeric(pos_cov)
                                )
 
p <- ggplot(data =dfForPlot
                    ,aes(x = clusters
                         ,y =  genes_expression
                         )) 
                p+geom_point()+
labs(col="Cluster")+labs(x="Cell")+labs(y="Number of position covered") +ggtitle("QC")
pos_cov_20000=pos_cov[pos_cov>number_positions]
somma_qc=somma[pos_cov>number_positions,]


##Second filtering step
if (filtering==1){
all_pos_update=apply(somma_qc,2,function(x){
    if(sum(x>number_reads)==length(row.names(somma_qc))){return (TRUE)}
  
  else{return (FALSE)}  
})

somma_qc_qc=somma_qc[,all_pos_update]}

if (filtering==2){
  
  all_pos_update=apply(somma_qc,2,function(x){
  clu_1=x[row.names(somma_qc)%in%cell_clu_1]
  
  clu_3=x[row.names(somma_qc)%in%cell_clu_3]
  
  clu_4=x[row.names(somma_qc)%in%cell_clu_4]
  
    if((sum(clu_1>number_reads)>0.5*length(clu_1))&(sum(clu_3>number_reads)>0.5*length(clu_3))&(sum(clu_4>number_reads)>0.5*length(clu_4))){return (TRUE)}
  
  else{return (FALSE)}  
})
  
 

somma_qc_qc=somma_qc[,all_pos_update]



}

 if(selection==TRUE){
   test_cell=apply(somma_qc_qc,1,function(x){sum(x>number_reads)})

cell_common=row.names(somma_qc_qc)[which(test_cell==dim(somma_qc_qc)[2])]



somma_qc_qc=somma_qc_qc[cell_common,] 
    
  }
indici=apply(somma_qc_qc,2,function(x){
  logico=which(x>number_reads)
  
  return((logico))
})
#save(somma_qc_qc,file="somma_qc_qc_antonio.Rda")
#normalization
all_posizioni_qc=all_posizioni[row.names(somma_qc_qc),colnames(all_posizioni)%in%colnames(somma_qc_qc)]

#save(all_posizioni_qc,file="all_posizioni_qc_antonio.Rda")
prova_new=matrix(0,ncol=length(colnames(all_posizioni_qc)),nrow = length(row.names(all_posizioni_qc)))
colnames(prova_new)=vedere_all[colnames(all_posizioni)%in%colnames(somma_qc_qc)]
row.names(prova_new)=row.names(somma_qc_qc)
colnames(all_posizioni_qc)=vedere_all[colnames(all_posizioni)%in%colnames(somma_qc_qc)]

for (i in colnames(all_posizioni_qc)){
  
  
  
prova_new[,i]=all_posizioni_qc[,i]/(somma_qc_qc[,substring(i,1,nchar(i)-4)]+0.0000000001)
  }
  
all_posizioni_qc_norm=prova_new

###Compute Entropy
prova_5=matrix(0,ncol=length(colnames(somma_qc_qc)),nrow=length(row.names(all_posizioni_qc_norm)))
colnames(prova_5)=colnames(somma_qc_qc)
row.names(prova_5)=row.names(all_posizioni_qc_norm)
colnames(all_posizioni_qc_norm)=vedere[vedere_all%in%colnames(all_posizioni_qc)]
for (i in colnames(somma_qc_qc)){
  
prova_5[,i]=apply(all_posizioni_qc_norm[,colnames(all_posizioni_qc_norm)%in%i],1,function(x){
  x=as.numeric(as.vector(x))
  if (sum(x)==0){return(-1)}
  else{
  
  return(1-max(x))
  }
})

}



prova_entro=prova_5
entropia=prova_entro




return(list(entropia,prova_new,indici))

}

plot_heteroplasmy_cell_competition=function(position,entropia_matrix,cluster,indici){
  position=colnames(entropia_matrix)[colnames(entropia_matrix)==position]
  cluster=cluster[as.numeric(indici[[position]])]
  mutazione=entropia_matrix[as.numeric(indici[[position]]),position]
dati_scatter_1=data.frame(as.character(cluster),mutazione)


ggplot(data =dati_scatter_1
                    ,aes(x =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE)
                         ,y = mutazione
                         ,col =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE))) +
geom_boxplot(width=0.9,alpha=0.8)+
labs(col="Cluster")+labs(x="Cluster")+labs(y="Heteroplasmy") +ggtitle(position) +scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Winner Epiblast","Intermediate", "Loser Epiblast"))

}

plot_allele_frequency_cell_competition=function(position,entropia_matrix,allele_matrix,cluster,indici,number){
  position=colnames(entropia_matrix)[colnames(entropia_matrix)==position]
 
  cluster=cluster[as.numeric(indici[[position]])]
  a=position
allele=colnames(allele_matrix)[grep(a,colnames(allele_matrix))]

mutazione_1=allele_matrix[as.numeric(indici[[position]]),allele[1]]
dati_scatter_1=data.frame(cluster,mutazione_1)
  
plot_1=ggplot(data =dati_scatter_1
                    ,aes(x =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE)
                         ,y = mutazione_1
                         ,col =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE))) +
geom_boxplot(width=0.9,alpha=0.8) + 
  geom_point()+labs(col="Cluster")+labs(y="Allele Frequency")+labs(col="Cluster")+labs(x="Cluster")+labs(y="Allele frequency") +ggtitle(allele[1])+ylim(0,1)+scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Winner Epiblast","Intermediate", "Loser Epiblast"))+theme(axis.text=element_text(size=number))



mutazione_2=allele_matrix[as.numeric(indici[[position]]),allele[2]]
dati_scatter_2=data.frame(cluster,mutazione_2)
  
plot_2=ggplot(data =dati_scatter_2
                    ,aes(x =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE)
                         ,y = mutazione_2
                         ,col =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE))) +
geom_boxplot(width=0.9,alpha=0.8) + 
  geom_point()+labs(col="Cluster")+labs(y="Allele Frequency")+labs(col="Cluster")+labs(x="Cluster")+labs(y="Allele frequency") +ggtitle(allele[2])+ylim(0,1)+scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Winner Epiblast","Intermediate", "Loser Epiblast"))+theme(axis.text=element_text(size=number))



mutazione_3=allele_matrix[as.numeric(indici[[position]]),allele[3]]
dati_scatter_3=data.frame(cluster,mutazione_3)
  
plot_3=ggplot(data =dati_scatter_3
                    ,aes(x =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE)
                         ,y = mutazione_3
                         ,col =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE))) +
geom_boxplot(width=0.9,alpha=0.8) + 
  geom_point()+labs(col="Cluster")+labs(y="Allele Frequency")+labs(col="Cluster")+labs(x="Cluster")+labs(y="Allele frequency") +ggtitle(allele[3])+ylim(0,1)+scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Winner Epiblast","Intermediate", "Loser Epiblast"))+theme(axis.text=element_text(size=number))



mutazione_4=allele_matrix[as.numeric(indici[[position]]),allele[4]]
dati_scatter_4=data.frame(cluster,mutazione_4)
  
plot_4=ggplot(data =dati_scatter_4
                    ,aes(x =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE)
                         ,y = mutazione_4
                         ,col =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE))) +
geom_boxplot(width=0.9,alpha=0.8) + 
  geom_point()+labs(col="Cluster")+labs(y="Allele Frequency")+labs(col="Cluster")+labs(x="Embryo")+labs(y="Allele frequency") +ggtitle(allele[4])+ylim(0,1)+scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Winner Epiblast","Intermediate", "Loser Epiblast"))+theme(axis.text=element_text(size=number))




grid.arrange(plot_1,plot_2,plot_3,plot_4 ,
             ncol = 2, nrow = 2)


}

gam_fit_cell_competition=function(entropia_matrix,indici,time,min_heteroplasmy=0.01,min_cells=10){
position=colnames(entropia_matrix)
considero=rep(0,length(position))
for (i in 1:length(position)){
Y=data.frame(t(entropia_matrix[as.numeric(indici[[position[i]]]),position[i]]))
considero[i]=sum(Y>min_heteroplasmy)
considero[considero]
}
entropia_matrix_select=entropia_matrix[,which(considero>min_cells)]

position=colnames(entropia_matrix_select)

p_value=rep(0,length(position))

for (i in 1:length(position)){
  Y=data.frame(t(entropia_matrix_select[as.numeric(indici[[position[i]]]),position[i]]))
t=time[as.numeric(indici[[position[i]]])]

#Run GAM with LOESS function for all the genes: save the p-value of parametric ANOVA 
#and the fitted expression values for each genes
gam.res <- apply(Y, 1, function(z){
  z=as.numeric(as.vector(z))
    d <- data.frame(z=z, t=t)
    tmp <- gam(z ~ lo(t), data=d)
    p <- summary(tmp)[4][[1]][1,5]  #p-value parametric ANOVA
    f<-fitted(tmp)
    #p2 <- summary(tmp)[3][[1]][2,3]  #p-value non-parametric ANOVA
    c(p,f)
    #c(p1,p2)   #If we want to return both the parametric and non-parametric p-values
})
genes.table<-data.frame(genes.names=rownames(Y))
genes.table$pvals<-gam.res[1,]
genes.table$FDR<-p.adjust(genes.table$pvals, method="fdr") #Adjust p-values
genes.table<-genes.table[order(genes.table$FDR),] #Order the genes according to the FDR
genes.table$genes.names <- as.character(genes.table$genes.names)
row.names(genes.table)=genes.table$genes.names


results.gam.tot=genes.table


#Save the matrix of fitted values
gam.fitted<-gam.res[-1,]


#results_gam=results.gam.tot['genes.names']
results_gam_nomi=results.gam.tot$genes.names
gam_fitted=gam.fitted
p_value[i]=results.gam.tot$pvals
}
fdr_update=p.adjust(p_value, method="fdr") 
all_fdr=data.frame(fdr_update,colnames(entropia_matrix_select))
colnames(all_fdr)=c('FDR_value',"Position")
row.names(all_fdr)=colnames(entropia_matrix_select)
all_fdr=all_fdr[order(all_fdr$'FDR_value'),]
return(all_fdr)

}

plot_losing_score=function(position,entropia_matrix,cluster,time,fit,indici){
position=colnames(entropia_matrix)[colnames(entropia_matrix)==position]
cluster=cluster[as.numeric(indici[[position]])]
cluster=factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE)
mutation=entropia_matrix[as.numeric(indici[[position]]),position]
time=time[as.numeric(indici[[position]])]
dati_scatter_1=data.frame(time,mutation)
plot<-ggplot(dati_scatter_1, aes(x=time, y=mutation)) + geom_point(aes(colour = (cluster)))+labs(col="Cluster")+labs(x="Losing Score")+labs(y="Heteroplasmy") +scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Winning Epiblast","Intermediate", "Losing Epiblast"))+ggtitle(paste(as.character(position),"adjusted p value=",round(fit[position,1],(floor(-log10(fit[position,1])))+1),sep=" "))+
  geom_smooth(method="loess",se=F,color="black",fullrange=F)
plot
}

plot_batch=function(position,entropia_matrix,batch,colour,indici,cluster,number){
  position=colnames(entropia_matrix)[colnames(entropia_matrix)==position]
  batch=batch[as.numeric(indici[[position]])]
  mutazione=entropia_matrix[as.numeric(indici[[position]]),position]
  colour=colour[as.numeric(indici[[position]])]
  cluster=cluster[as.numeric(indici[[position]])]
dati_scatter_1=data.frame(batch,mutazione)


ggplot(data =dati_scatter_1
                    ,aes(x = batch
                         ,y = mutazione
                         ,col =factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE))) +
geom_boxplot(width=0.9,alpha=0.8)+
labs(col="Cluster")+labs(x="Batch")+labs(y="Heteroplasmy") +ggtitle(position) +scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Winner Epiblast","Intermediate", "Loser Epiblast"))+theme(axis.text=element_text(size=number))

}

plot_losing_score_average=function(average,cluster,time,indici,p_value){
cluster=cluster[as.numeric(indici)]
cluster=factor(cluster,levels=c("Winner Epiblast","Intermediate","Loser Epiblast"),ordered = TRUE)
mutation=average[as.numeric(indici)]
time=time[as.numeric(indici)]
dati_scatter_1=data.frame(time,mutation)
plot<-ggplot(dati_scatter_1, aes(x=time, y=mutation)) + geom_point(aes(colour = (cluster)))+labs(col="Cluster")+labs(x="Losing Score")+labs(y="Heteroplasmy") +scale_color_manual(values=c("#DD6400",  "#0000FF","#006400"),labels=c("Winning Epiblast","Intermediate", "Losing Epiblast"))+ggtitle(paste('Average',p_value,sep=" "))+
  geom_smooth(method="loess",se=F,color="black",fullrange=F)
plot
}

Only CI treated epiblast cells

entropia_matrix_fmk=entropy_method(all_pos,cells_fmk,vedere_all,vedere_numb,50,2000,filtering = 2,cell_clu_1=cell_clu_1,cell_clu_3=cell_clu_3,cell_clu_4=cell_clu_4,selection = FALSE)

entropia_matrix_ci=entropia_matrix_fmk[[1]]
allele_matrix_ci=entropia_matrix_fmk[[2]]
indici_ci=entropia_matrix_fmk[[3]]
cluster_ci=as.character(provo_4[row.names(entropia_matrix_ci),]$cluster)
cluster_ci[cluster_ci=="1"]="Winner Epiblast"
cluster_ci[cluster_ci=="3"]="Intermediate"
cluster_ci[cluster_ci=="4"]="Loser Epiblast"
condition_ci=as.character(provo_4[row.names(entropia_matrix_ci),]$condizione)
condition_ci[condition_ci=='FMK']='CI-treated'
condition_ci[condition_ci=='DMSO']='DMSO-treated'

Extended Figure 7 M

  library(karyoploteR)

## Loading required package: regioneR

setwd(base_path)
mt_name=read.table("useful_mt_names.txt",sep="\t",header = T)
mt_name=mt_name[mt_name$Chromosome.scaffold.name=="MT",]
nomi=mt_name$Gene.name
p=rep(0,length(nomi))
for (j in 1:length(nomi)){
i=which(mt_name$Gene.name==nomi[j])
bo=seq(mt_name$Gene.start..bp.[i],mt_name$Gene.end..bp.[i])
bo=as.character(bo)
print(paste(sum(colnames(entropia_matrix_ci)%in%bo),nomi[i],sep=" "))
if (sum(colnames(entropia_matrix_ci)%in%bo)>0){
  p[i]=nomi[i]
}
  
}

## [1] "0 mt-Tf"
## [1] "509 mt-Rnr1"
## [1] "0 mt-Tv"
## [1] "1521 mt-Rnr2"
## [1] "0 mt-Tl1"
## [1] "869 mt-Nd1"
## [1] "0 mt-Ti"
## [1] "0 mt-Tq"
## [1] "0 mt-Tm"
## [1] "0 mt-Nd2"
## [1] "0 mt-Tw"
## [1] "0 mt-Ta"
## [1] "0 mt-Tn"
## [1] "0 mt-Tc"
## [1] "0 mt-Ty"
## [1] "1288 mt-Co1"
## [1] "0 mt-Ts1"
## [1] "0 mt-Td"
## [1] "0 mt-Co2"
## [1] "0 mt-Tk"
## [1] "0 mt-Atp8"
## [1] "0 mt-Atp6"
## [1] "0 mt-Co3"
## [1] "0 mt-Tg"
## [1] "0 mt-Nd3"
## [1] "0 mt-Tr"
## [1] "0 mt-Nd4l"
## [1] "63 mt-Nd4"
## [1] "0 mt-Th"
## [1] "0 mt-Ts2"
## [1] "0 mt-Tl2"
## [1] "0 mt-Nd5"
## [1] "0 mt-Nd6"
## [1] "0 mt-Te"
## [1] "942 mt-Cytb"
## [1] "0 mt-Tt"
## [1] "0 mt-Tp"

mt_name_small=mt_name[mt_name$Gene.name%in%p,]
start_plot=c(mt_name_small$Gene.start..bp.,mt_name_small$Gene.end..bp.)
nomi_plot=paste0(mt_name_small$Gene.name,"-start")
nomi_plot_2=paste0(mt_name_small$Gene.name,"-end")
names(start_plot)=c(nomi_plot,nomi_plot_2)
start_plot=sort(start_plot)

nomi_plot=rep(mt_name_small$Gene.name,2)
markers <- data.frame(chr=rep("MT", 12), pos=start_plot, labels=names(start_plot))

start_inizio=mt_name$Gene.start..bp.
start_fine=mt_name$Gene.end..bp.

for (i in 2:(length(start_inizio))){
  if (start_inizio[i]<start_fine[i-1]){
    print(i)
    start_inizio[i]=start_fine[i-1]}
  
}

## [1] 7
## [1] 8
## [1] 11
## [1] 17
## [1] 22
## [1] 28
## [1] 33

start_due=rep(0,length(start_inizio))
fine_due=rep(0,length(start_inizio))
for (i in 1:(length(start_inizio)-1)){
  if (start_fine[i]!=start_inizio[i+1]){
 start_due[i]=min(c(start_fine[i],start_inizio[i+1]))
 fine_due[i]=max(c(start_fine[i],start_inizio[i+1]))
}}

colore_uno=rep("red",length(start_inizio))
names(start_inizio)=colore_uno
start_due[37]=start_fine[37]
fine_due[37]=16299




colore_due=rep("black",length(start_due))
names(start_due)=colore_due
fine_due=fine_due[fine_due!=0]
start_due=start_due[start_due!=0]


start_all=c(start_inizio,start_due)
start_all=sort(start_all)
fine_all=c(start_fine,fine_due)
fine_all=sort(fine_all)
name_base=paste0("A",seq(1:65))

colore_new=names(start_all)

colore_uno=rep("red",length(start_inizio))
names(start_inizio)=colore_uno
colore_bo=names(start_inizio)

#provo=vedo_mt[1:2,]
#provo$start=c(1,1000)
#provo$end=c(1000,16299)

all_base=seq(1,16299)
all_base=as.character(all_base)
name_all_base=rep(0,16299)
name_all_base[all_base%in%colnames(entropia_matrix_ci)]="Covered"
name_all_base[!(all_base%in%colnames(entropia_matrix_ci))]="Not_Covered"
name_all_base=paste0(name_all_base,all_base)
colore=rep(0,16299)
colore[all_base%in%colnames(entropia_matrix_ci)]="red"
colore[!(all_base%in%colnames(entropia_matrix_ci))]="black"
colore_plot=rep(0,16299)
colore_plot[all_base%in%colnames(entropia_matrix_ci)]="acen"
colore_plot[!all_base%in%colnames(entropia_matrix_ci)]="acen"
#vedo_mt_new=data.frame(rep("MT",length(37)),all_base,all_base,name_all_base,colore_plot)

provo_vedo=data.frame(rep("MT",length(65)),start_all,fine_all,name_base,colore_new)
#vedo_mt_new=data.frame(rep("MT",length(16299)),all_base,all_base,colore)
#colnames(provo_vedo)=colnames(vedo_mt)

#provo_vedo_due=provo_vedo[1:3,]
#provo_vedo_due$end[3]=16299
#provo_vedo_due=provo_vedo_due[-4]

custom.genome <- toGRanges(data.frame(chr=c("MT"), start=c(1), end=c(16299)))
#custom.cytobands <- toGRanges(vedo_mt_new)
#kp <- plotKaryotype(genome = custom.genome,cytobands =custom.cytobands )
#custom.cytobands <- toGRanges(provo_vedo)
#kp <- plotKaryotype(genome = custom.genome,cytobands = custom.cytobands)
kp <- plotKaryotype(genome = custom.genome )

df <- data.frame(chr=rep("MT",16299), start=all_base, end=all_base)
kpPlotRegions(kp, data=df, col=colore, border=colore, r0=0.3, r1=0.55)


#df_second <- data.frame(chr=c("MT"), start=start_inizio, end=start_fine)
#kpPlotRegions(kp, data=df_second, col=colore_bo, border=colore_bo, r0=0, r1=0.25,avoid.overlapping=F)

#markers <- data.frame(chr=rep("MT", 10), pos=(1:10*10), labels=paste0("Gene", 1:10))
#markers <- data.frame(chr=rep("MT", 37), pos=(mt_name$Gene.start..bp.), labels=mt_name$Gene.name)
markers <- data.frame(chr=rep("MT", 12), pos=start_plot, labels=names(start_plot))
kpAddBaseNumbers(kp,tick.dist=1000,minor.tick.dist=1000,add.units=T,tick.len = 30)

kpPlotMarkers(kp, chr=markers$chr, x=markers$pos, labels=markers$labels)

#plot_heteroplasmy_cell_competition('327',entropia_matrix_ci,cluster_ci,indici_ci)

#plot_heteroplasmy_cell_competition('326',entropia_matrix_ci,cluster_ci,indici_ci)


#plot_allele_frequency_cell_competition('326',entropia_matrix_ci,allele_matrix_ci,cluster_ci,indici_ci#,number=5)  

#plot_allele_frequency_cell_competition('327',entropia_matrix_ci,allele_matrix_ci,cluster_ci,indici_ci#,number=5)

Losing score analysis

library(gam)
#setwd("/Users/gabriele.lubatti/Desktop/Phd/Cell_Competition/Script")
#load("time_fmk_epi.Rda")
#load("cells_epi_fmk.Rda")
row.names(provo_4)=provo_4$nomi_vecchi
provo_6=provo_4[cells_epi_fmk,]
provo_6$tempo=time_fmk_epi
row.names(provo_6)=provo_6$Cellname
provo_7=provo_6[row.names(entropia_matrix_ci),]

fit_result=gam_fit_cell_competition(entropia_matrix_ci,indici_ci,provo_7$tempo,0.01,10)

fit_result_final=fit_result[fit_result$FDR_value<0.001,]

Figure 6 B, C, D, E, F, G and Extended Figure 7 A, B, C, D, E

p=list()
for(i in row.names(fit_result_final)){
  q=plot_losing_score(i,entropia_matrix_ci,cluster_ci,provo_7$tempo,fit_result_final,indici_ci)
  p=list(p,q)
}

p

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## `geom_smooth()` using formula 'y ~ x'

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## `geom_smooth()` using formula 'y ~ x'

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## `geom_smooth()` using formula 'y ~ x'

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## `geom_smooth()` using formula 'y ~ x'

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## `geom_smooth()` using formula 'y ~ x'

Figure 6 A

average=apply(entropia_matrix_ci[,row.names(fit_result_final)],1,mean)

index_average=Reduce(intersect, list(as.numeric(indici_ci[[row.names(fit_result_final)[1]]]),as.numeric(indici_ci[[row.names(fit_result_final)[2]]]),as.numeric(indici_ci[[row.names(fit_result_final)[3]]]),as.numeric(indici_ci[[row.names(fit_result_final)[4]]]),as.numeric(indici_ci[[row.names(fit_result_final)[5]]]),as.numeric(indici_ci[[row.names(fit_result_final)[6]]]),as.numeric(indici_ci[[row.names(fit_result_final)[7]]]),as.numeric(indici_ci[[row.names(fit_result_final)[8]]]),as.numeric(indici_ci[[row.names(fit_result_final)[9]]]),as.numeric(indici_ci[[row.names(fit_result_final)[10]]]),as.numeric(indici_ci[[row.names(fit_result_final)[11]]])))

z=average[as.numeric(index_average)]
t=provo_7$tempo[as.numeric(index_average)]

#Run GAM with LOESS function for all the genes: save the p-value of parametric ANOVA 
#and the fitted expression values for each genes
    d <- data.frame(z=z, t=t)
    tmp <- gam(z ~ lo(t), data=d)
    p_value <- summary(tmp)[4][[1]][1,5]  #p-value parametric ANOVA
    f<-fitted(tmp)
    #p2 <- summary(tmp)[3][[1]][2,3]  #p-value non-parametric ANOVA
    #c(p,f)
    #c(p1,p2)   #If we want to return both the parametric and non-parametric p-values

plot_losing_score_average(average,cluster_ci,provo_7$tempo,index_average,p_value)

## `geom_smooth()` using formula 'y ~ x'

Extended Figure 7 F, G, H, I, J, K

utile=provo_7
numero=rep(0,length(utile$cluster))
numero[utile$cluster==1&utile$Cell_type=="FMK 3"]="a)Win 1"
numero[utile$cluster==1&utile$Cell_type=="FMK 4"]="a)Win 5"
numero[utile$cluster==1&utile$Cell_type=="FMK 5"]="a)Win 4"
numero[utile$cluster==1&utile$Cell_type=="FMK 6"]="a)Win 2"
numero[utile$cluster==1&utile$Cell_type=="FMK 7"]="a)Win 3"
numero[utile$cluster==3&utile$Cell_type=="FMK 3"]="b)Int 1"
numero[utile$cluster==3&utile$Cell_type=="FMK 4"]="b)Int 5"
numero[utile$cluster==3&utile$Cell_type=="FMK 5"]="b)Int 4"
numero[utile$cluster==3&utile$Cell_type=="FMK 6"]="b)Int 2"
numero[utile$cluster==3&utile$Cell_type=="FMK 7"]="b)Int 3"
numero[utile$cluster==4&utile$Cell_type=="FMK 3"]="c)Los 1"
numero[utile$cluster==4&utile$Cell_type=="FMK 4"]="c)Los 5"
numero[utile$cluster==4&utile$Cell_type=="FMK 5"]="c)Los 4"
numero[utile$cluster==4&utile$Cell_type=="FMK 6"]="c)Los 2"
numero[utile$cluster==4&utile$Cell_type=="FMK 7"]="c)Los 3"


colore=rep(0,length(numero))
colore[grep("Int",numero)]="#0000FF"
colore[grep("Los",numero)]="#006400"
colore[grep("Win",numero)]="#DD6400"

plot_batch('326',entropia_matrix_ci,numero,colore,indici_ci,cluster_ci,number=6)

plot_batch('327',entropia_matrix_ci,numero,colore,indici_ci,cluster_ci,number=6)

plot_batch('303',entropia_matrix_ci,numero,colore,indici_ci,cluster_ci,number=6)

plot_batch('304',entropia_matrix_ci,numero,colore,indici_ci,cluster_ci,number=6)

plot_batch('305',entropia_matrix_ci,numero,colore,indici_ci,cluster_ci,number=6)

plot_batch('300',entropia_matrix_ci,numero,colore,indici_ci,cluster_ci,number=6)

Figure 6 I

index_rnr1=Reduce(intersect, list(as.numeric(indici_ci[[row.names(fit_result_final)[1]]]),as.numeric(indici_ci[[row.names(fit_result_final)[2]]]),as.numeric(indici_ci[[row.names(fit_result_final)[6]]]),as.numeric(indici_ci[[row.names(fit_result_final)[7]]]),as.numeric(indici_ci[[row.names(fit_result_final)[8]]]),as.numeric(indici_ci[[row.names(fit_result_final)[11]]])))

position_rnr1=c(row.names(fit_result_final)[1],row.names(fit_result_final)[2],row.names(fit_result_final)[6],row.names(fit_result_final)[7],row.names(fit_result_final)[8],row.names(fit_result_final)[11])

u=entropia_matrix_ci[index_rnr1,position_rnr1]

studio=data.frame(u)



#la parte sulla correlazione non viene per il formato del dataframe
correlazione=cor(studio,method="spearman")
colnames(correlazione)=paste((substr(colnames(correlazione),2,nchar(colnames(correlazione)))),c("mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1"))
row.names(correlazione)=paste((substr(row.names(correlazione),2,nchar(row.names(correlazione)))),c("mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1"))
ht21 = Heatmap(correlazione# heat.vals_Mutant_Paired #
               ,cluster_rows = TRUE
                  , col= colorRamp2(c(0, round(max(correlazione))), c("white", "red"))
                  , name = "Spearman correlation"
                  , column_title = "Spearman correlation FMK cells (215 cells)"
                  #, cluster_columns = as.dendrogram(my.tree)
, cluster_columns = TRUE
                  #, cluster_rows = TRUE #=as.dendrogram(as.numeric(row.names(log_data_tpm)[1:10]) )
                  #, top_annotation = haCol1
                  , row_names_gp = gpar(fontsize = 8
                                       #,col = geneColors
                   )
,column_names_gp = gpar(fontsize=8)
                  , show_column_names = T
                  , show_row_names = T
 

)
    
  
draw(ht21
    #,column_title = "Absolute values", column_title_side = "top"
)

Extended Figure 7 I

index_top=Reduce(intersect, list(as.numeric(indici_ci[[row.names(fit_result_final)[1]]]),as.numeric(indici_ci[[row.names(fit_result_final)[2]]]),as.numeric(indici_ci[[row.names(fit_result_final)[3]]]),as.numeric(indici_ci[[row.names(fit_result_final)[4]]]),as.numeric(indici_ci[[row.names(fit_result_final)[5]]]),as.numeric(indici_ci[[row.names(fit_result_final)[6]]]),as.numeric(indici_ci[[row.names(fit_result_final)[7]]]),as.numeric(indici_ci[[row.names(fit_result_final)[8]]]),as.numeric(indici_ci[[row.names(fit_result_final)[9]]]),as.numeric(indici_ci[[row.names(fit_result_final)[10]]]),as.numeric(indici_ci[[row.names(fit_result_final)[11]]])))

position_top=c(row.names(fit_result_final)[1],row.names(fit_result_final)[2],row.names(fit_result_final)[3],row.names(fit_result_final)[4],row.names(fit_result_final)[5],row.names(fit_result_final)[6],row.names(fit_result_final)[7],row.names(fit_result_final)[8],row.names(fit_result_final)[9],row.names(fit_result_final)[10],row.names(fit_result_final)[11])

u=entropia_matrix_ci[index_top,position_top]

studio=data.frame(u)



#la parte sulla correlazione non viene per il formato del dataframe
correlazione=cor(studio,method="spearman")
colnames(correlazione)=paste((substr(colnames(correlazione),2,nchar(colnames(correlazione)))),c("mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1"))
row.names(correlazione)=paste((substr(row.names(correlazione),2,nchar(row.names(correlazione)))),c("mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1","mt-Rnr1"))
ht21 = Heatmap(correlazione# heat.vals_Mutant_Paired #
               ,cluster_rows = TRUE
                  , col= colorRamp2(c(0, round(max(correlazione))), c("white", "red"))
                  , name = "Spearman correlation"
                  , column_title = "Spearman correlation FMK cells (214 cells)"
                  #, cluster_columns = as.dendrogram(my.tree)
, cluster_columns = TRUE
                  #, cluster_rows = TRUE #=as.dendrogram(as.numeric(row.names(log_data_tpm)[1:10]) )
                  #, top_annotation = haCol1
                  , row_names_gp = gpar(fontsize = 8
                                       #,col = geneColors
                   )
,column_names_gp = gpar(fontsize=8)
                  , show_column_names = T
                  , show_row_names = T
 

)
    
  
draw(ht21
    #,column_title = "Absolute values", column_title_side = "top"
)

Figure 6 H

identification_cluster=function(a){


index_rnr1=Reduce(intersect, list(as.numeric(indici_ci[[row.names(fit_result_final)[1]]]),as.numeric(indici_ci[[row.names(fit_result_final)[2]]]),as.numeric(indici_ci[[row.names(fit_result_final)[6]]]),as.numeric(indici_ci[[row.names(fit_result_final)[7]]]),as.numeric(indici_ci[[row.names(fit_result_final)[8]]]),as.numeric(indici_ci[[row.names(fit_result_final)[11]]])))

position_rnr1=c(row.names(fit_result_final)[1],row.names(fit_result_final)[2],row.names(fit_result_final)[6],row.names(fit_result_final)[7],row.names(fit_result_final)[8],row.names(fit_result_final)[11])



#indici_mt=c(indici_nuovi[1:6],indici_nuovi[8])
rnr1=entropia_matrix_ci[index_rnr1,position_rnr1]
rnr1_cluster=apply(rnr1,1,function(x){
  x=as.numeric(as.vector(x))
  x=mean(x)
 return (x)
})
provo_8=provo_7[index_rnr1,]
Cluster_col=provo_8$cluster
Cluster_col[Cluster_col==1]="1)Winning Epiblast"
Cluster_col[Cluster_col==4]="3)Losing Epiblast"
Cluster_col[Cluster_col==3]="2)Intermediate"
Cluster=as.factor(Cluster_col)
frequenze=rep(0,length(rnr1_cluster))
frequenze[rnr1_cluster>a]=1
frequenze[rnr1_cluster<=a]=0
clu_1=sum(frequenze[Cluster=="1)Winning Epiblast"])/(sum(Cluster=="1)Winning Epiblast"))
print((sum(Cluster=="1)Winning Epiblast")))
clu_3=sum(frequenze[Cluster=="2)Intermediate"])/(sum(Cluster=="2)Intermediate"))
print((sum(Cluster=="2)Intermediate")))
clu_4=sum(frequenze[Cluster=="3)Losing Epiblast"])/(sum(Cluster=="3)Losing Epiblast"))
print(sum(Cluster=="3)Losing Epiblast"))
frequenze_norm=c(clu_1,clu_3,clu_4)
Cluster_name=levels(Cluster)
Cluster=Cluster_name

dati_cluster=data.frame(frequenze_norm,Cluster)

p=ggplot(dati_cluster,aes(x=Cluster, y=frequenze_norm,fill=Cluster)) + geom_bar(stat="identity")+scale_fill_manual(values=c("#DD6400",  "#0000FF","#006400"))+xlab(NULL)+
  ylab("Percentage of cells ")+ggtitle("Percentage of cells in cluster carrying significant mutations in mt-Rnr1")
p
}

identification_cluster(0.01) 

## [1] 68
## [1] 92
## [1] 55

sessionInfo()

## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Mojave 10.14.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
##  [1] grid      parallel  stats4    splines   stats     graphics  grDevices
##  [8] utils     datasets  methods   base     
## 
## other attached packages:
##  [1] karyoploteR_1.14.1          regioneR_1.20.1            
##  [3] cowplot_1.1.1               repr_1.1.0                 
##  [5] UpSetR_1.4.0                scales_1.1.1               
##  [7] clustree_0.4.3              ggraph_2.0.5               
##  [9] scater_1.16.2               SeuratObject_4.0.0         
## [11] Seurat_4.0.1                fpc_2.2-8                  
## [13] edgeR_3.30.3                limma_3.44.3               
## [15] ggforce_0.3.3               circlize_0.4.12            
## [17] destiny_3.2.0               viridis_0.5.1              
## [19] viridisLite_0.3.0           gridExtra_2.3              
## [21] ComplexHeatmap_2.4.3        umap_0.2.6.0               
## [23] dynamicTreeCut_1.63-1       ggplot2_3.3.3              
## [25] scran_1.16.0                SingleCellExperiment_1.10.1
## [27] SummarizedExperiment_1.18.2 DelayedArray_0.14.1        
## [29] matrixStats_0.58.0          Biobase_2.48.0             
## [31] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2        
## [33] IRanges_2.22.2              S4Vectors_0.26.1           
## [35] BiocGenerics_0.34.0         gam_1.20                   
## [37] foreach_1.5.1              
## 
## loaded via a namespace (and not attached):
##   [1] rsvd_1.0.3                vcd_1.4-7                
##   [3] Hmisc_4.5-0               ica_1.0-2                
##   [5] class_7.3-17              Rsamtools_2.4.0          
##   [7] lmtest_0.9-38             crayon_1.4.1             
##   [9] laeken_0.5.1              spatstat.core_2.0-0      
##  [11] MASS_7.3-52               nlme_3.1-149             
##  [13] backports_1.2.1           rlang_0.4.10             
##  [15] XVector_0.28.0            ROCR_1.0-11              
##  [17] readxl_1.3.1              irlba_2.3.3              
##  [19] smoother_1.1              BiocParallel_1.22.0      
##  [21] rjson_0.2.20              bit64_4.0.2              
##  [23] glue_1.4.2                sctransform_0.3.2        
##  [25] vipor_0.4.5               spatstat.sparse_2.0-0    
##  [27] AnnotationDbi_1.50.3      spatstat.geom_2.0-1      
##  [29] haven_2.3.1               tidyselect_1.1.0         
##  [31] rio_0.5.16                fitdistrplus_1.1-3       
##  [33] XML_3.99-0.6              tidyr_1.1.3              
##  [35] zoo_1.8-9                 GenomicAlignments_1.24.0 
##  [37] xtable_1.8-4              RcppHNSW_0.2.0           
##  [39] magrittr_2.0.1            evaluate_0.14            
##  [41] zlibbioc_1.34.0           rstudioapi_0.13          
##  [43] miniUI_0.1.1.1            sp_1.4-2                 
##  [45] rpart_4.1-15              ensembldb_2.12.1         
##  [47] RcppEigen_0.3.3.9.1       shiny_1.6.0              
##  [49] BiocSingular_1.4.0        xfun_0.17                
##  [51] askpass_1.1               clue_0.3-57              
##  [53] cluster_2.1.0             tidygraph_1.2.0          
##  [55] pcaMethods_1.80.0         tibble_3.1.0             
##  [57] ggrepel_0.9.1             biovizBase_1.36.0        
##  [59] listenv_0.8.0             Biostrings_2.56.0        
##  [61] png_0.1-7                 future_1.21.0            
##  [63] withr_2.4.1               bitops_1.0-6             
##  [65] ranger_0.12.1             plyr_1.8.6               
##  [67] cellranger_1.1.0          AnnotationFilter_1.12.0  
##  [69] e1071_1.7-6               dqrng_0.2.1              
##  [71] pillar_1.5.1              GlobalOptions_0.1.2      
##  [73] GenomicFeatures_1.40.1    flexmix_2.3-17           
##  [75] kernlab_0.9-29            scatterplot3d_0.3-41     
##  [77] TTR_0.24.0                GetoptLong_1.0.2         
##  [79] DelayedMatrixStats_1.10.1 xts_0.12-0               
##  [81] vctrs_0.3.7               ellipsis_0.3.1           
##  [83] generics_0.1.0            tools_4.0.2              
##  [85] foreign_0.8-80            beeswarm_0.2.3           
##  [87] munsell_0.5.0             tweenr_1.0.2             
##  [89] proxy_0.4-25              fastmap_1.1.0            
##  [91] compiler_4.0.2            abind_1.4-5              
##  [93] httpuv_1.5.5              rtracklayer_1.48.0       
##  [95] plotly_4.9.3              GenomeInfoDbData_1.2.3   
##  [97] lattice_0.20-41           deldir_0.2-10            
##  [99] utf8_1.2.1                later_1.1.0.1            
## [101] dplyr_1.0.5               BiocFileCache_1.12.1     
## [103] jsonlite_1.7.2            ggplot.multistats_1.0.0  
## [105] pbapply_1.4-3             carData_3.0-4            
## [107] lazyeval_0.2.2            promises_1.2.0.1         
## [109] car_3.0-9                 latticeExtra_0.6-29      
## [111] goftest_1.2-2             spatstat.utils_2.1-0     
## [113] reticulate_1.18           checkmate_2.0.0          
## [115] rmarkdown_2.3             openxlsx_4.1.5           
## [117] statmod_1.4.34            Rtsne_0.15               
## [119] forcats_0.5.0             dichromat_2.0-0          
## [121] BSgenome_1.56.0           uwot_0.1.10              
## [123] igraph_1.2.6              survival_3.2-3           
## [125] yaml_2.2.1                prabclus_2.3-2           
## [127] htmltools_0.5.1.1         memoise_1.1.0            
## [129] VariantAnnotation_1.34.0  modeltools_0.2-23        
## [131] locfit_1.5-9.4            graphlayouts_0.7.1       
## [133] digest_0.6.27             assertthat_0.2.1         
## [135] mime_0.10                 rappdirs_0.3.3           
## [137] RSQLite_2.2.0             future.apply_1.7.0       
## [139] data.table_1.14.0         blob_1.2.1               
## [141] Formula_1.2-4             labeling_0.4.2           
## [143] ProtGenerics_1.20.0       RCurl_1.98-1.2           
## [145] hms_0.5.3                 colorspace_2.0-0         
## [147] base64enc_0.1-3           ggbeeswarm_0.6.0         
## [149] shape_1.4.5               nnet_7.3-14              
## [151] Rcpp_1.0.6                mclust_5.4.6             
## [153] RANN_2.6.1                fansi_0.4.2              
## [155] VIM_6.0.0                 parallelly_1.24.0        
## [157] R6_2.5.0                  ggridges_0.5.3           
## [159] lifecycle_1.0.0           zip_2.1.1                
## [161] curl_4.3                  leiden_0.3.7             
## [163] robustbase_0.93-6         Matrix_1.2-18            
## [165] RcppAnnoy_0.0.18          RColorBrewer_1.1-2       
## [167] iterators_1.0.13          stringr_1.4.0            
## [169] htmlwidgets_1.5.3         bamsignals_1.20.0        
## [171] polyclip_1.10-0           biomaRt_2.44.4           
## [173] purrr_0.3.4               mgcv_1.8-32              
## [175] globals_0.14.0            openssl_1.4.3            
## [177] htmlTable_2.1.0           patchwork_1.1.1          
## [179] codetools_0.2-16          prettyunits_1.1.1        
## [181] dbplyr_2.0.0              RSpectra_0.16-0          
## [183] gtable_0.3.0              DBI_1.1.0                
## [185] tensor_1.5                httr_1.4.2               
## [187] KernSmooth_2.23-17        stringi_1.5.3            
## [189] progress_1.2.2            reshape2_1.4.4           
## [191] farver_2.1.0              diptest_0.75-7           
## [193] ggthemes_4.2.0            hexbin_1.28.1            
## [195] bezier_1.1.2              xml2_1.3.2               
## [197] boot_1.3-25               BiocNeighbors_1.6.0      
## [199] scattermore_0.7           DEoptimR_1.0-8           
## [201] bit_4.0.4                 jpeg_0.1-8.1             
## [203] spatstat.data_2.1-0       pkgconfig_2.0.3          
## [205] knn.covertree_1.0         knitr_1.29